On 2014 Nov 23, Harri Hemila commented:

A comment is available at: http://hdl.handle.net/10138/16985

On 2015 Jul 20, Salzman Lab Journal Club commented:

This paper provides strong evidence for the dramatic functional change in protein coding enabled by precise RNA editing. An interesting followup would be to see if there are changes in expression levels of the editing guide RNA during the course of trypanosome development. Also, it would be interesting to know the level of expression of the dominant negative AEP1-GFP protein relative to endogenous levels of AEP1.

On 2015 Dec 06, Mark Bolland commented:

There are several errors/inconsistencies in the data related to treatment group numbers and numbers of falls and fractures in this paper. These include:

• 1. The text and Table 1 reports 121 participants in each treatment group, but Table 3 reports 120 for Ca and 122 for CaD.
• 2. Table 3 reports that 75 participants fell with Ca and 49 with CaD, but the breakdown of fallers in Table 3 sums to 71 for Ca and 53 for CaD.
• 3. The text reports the total number of falls as 171 with Ca and 76 with CaD, but Table 3 reports 169 with Ca and 106 with CaD.
• 4. The breakdown of total falls in Table 3 sums to at least 111 with CaD, which is greater than the total falls for CaD reported in the text (76) and Table 3 (106).
• 5. The text reports the mean number of falls per group as 1.41 with Ca and 0.63 with CaD which are incompatible with the reported total number of falls in the text and Table 3.
• 6. The text reports 13 participants with fracture with Ca whereas Table 3 reports 12.

These are discussed in two letters to the editor (Osteoporos Int 2015;26:2713 Bolland MJ, 2015 and Arch Osteoporos. 2015;10:43 Bolland MJ, 2015) with responses by the lead author to each letter (Osteoporos Int. 2015;26:2715-6 Pfeifer M, 2015 and Arch Osteoporos. 2015;10:42 Pfeifer M, 2015).

Given the unwillingness/inability of the author to provide consistent data from the trial, we think it should be excluded from systematic reviews or meta-analyses until consistent data are provided by the authors, with some explanation as to how the errors/inconsistencies occurred.

We have described the full sequence of events in a separate comment.

Mark Bolland, Andrew Grey. University of Auckland

On 2015 Dec 06, Mark Bolland commented:

This comment provides background information to an earlier comment.

We were interested in why different meta-analyses of vitamin D supplements came to different conclusions, and noticed that different meta-analyses used different data from this trial. We identified several errors/inconsistencies in the text and emailed the lead author requesting clarification about the data in March 2014. He responded that the data in the Tables were correct. We replied that there were inconsistencies within the Tables as well and asked for clarification, but he did not respond.

Therefore in April 2014, we contacted the editor of Osteoporosis International advising the editor of the inconsistencies and requested that they be corrected. We felt this is particularly important as it is a highly cited, influential trial reporting benefits of vitamin D supplements on falls, and the inconsistencies occur in the treatment group numbers and the primary and secondary outcomes, so have a major bearing of the interpretation of the trial results. Our primary concern was/is to use the correct data for the trial in meta-analyses. In May 2014, the editor passed on an extract of the lead author’s response and indicated that an erratum would be published. However, we felt the lead author’s response was inadequate because it left a number of uncorrected inconsistencies/errors in the text. We pointed this out to the editor. We are not sure what action the editor took, but no erratum was published. We followed up with 2 further emails to the editor over the next year, and in April 2015, the editor advised that it seemed unlikely that an erratum would be forthcoming.

Therefore, we requested the permission to summarize the issues in a very brief letter. The editor agreed and a short letter summarizing the six errors/inconsistencies in the article was published (Osteoporos Int 2015;26:2713 Bolland MJ, 2015) with a response from the author (Osteoporos Int. 2015;26:2715-6 Pfeifer M, 2015). Unfortunately, in his response the author chose to correct only 2 of the errors identified and introduced a further inconsistency.

In a further letter to the editor, we therefore highlighted the remaining errors/inconsistencies and the consequence of using different possible data combinations from this trial for meta-analyses. The editor indicated that the issues we raised were important but probably irresolvable and offered to publish the letter in Archives of Osteoporosis. We indicated that our preference was that the data were corrected in the original publication rather than our letter being published. We think it quite straightforward to make the simple necessary corrections to two tables and a couple of sentences of text. We feel it is essential that the corrections occur as the errors are in the most important data from the trial: the treatment group numbers and the primary outcome data for falls. The editor considered the issue further and then published our letter (Arch Osteoporos. 2015;10:43 Bolland MJ, 2015) along with a response from the author (Arch Osteoporos. 2015;10:42 Pfeifer M, 2015).

In this second response, the author has still not corrected the identified errors, so, because of the inconsistencies/errors in the data, it is not possible to be sure how many women were in each randomized treatment group, how many women had a fall during the trial, how many total falls occurred in each treatment group, or what is the breakdown of participants by numbers of falls (no falls, 1 fall, 2 falls, etc).

Therefore, we think that the study should be excluded from meta-analyses and systematic reviews and its results viewed with caution until consistent data are provided by the authors with some explanation as to how the errors/inconsistencies occurred.

Mark Bolland, Andrew Grey. University of Auckland

On 2014 Dec 18, Claudia Paiva commented:

Is it possible that the recurrency is overestimated because patients without recurrent events leave the hospital and are excluded from the study? 1334 cases were found and 127 chosen because they could be followed for long term... The rate of recurrency among high-tone controls is 11% (without vertigo) and 17% (without SN), while in the literature the total recurrency among SSNHL sufferers is approximately 2%.

Alternatively, it could be due to a higher rate of recurrency in Japan, since no western studies were made concerning the recurrency rates among low tone hearing loss in western countries. Do you know any broad study in Japan that show the total recurrency rate of SSNHL in Japan and percentages of high x low tone hearing loss among total SSNHL?

It is very important to define the rate of recurrency, so that the patient is aware of the possibility and plot a plan to face it with drs (in case it is high) or can live without the constant burden of "is it going to happen again?" in their heads (in case it is low).

Anyway, these are just considerations made by a worried low tone SSNHL patient and a scientist from another field.

On 2016 Apr 25, Sergio Uribe commented:

Can caries in primary teeth predict caries in permanent teeth in Sweden? Yes considering this paper; No according to https://www.ncbi.nlm.nih.gov/pubmed/23235131

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT005725079. We believe the correct ID, which we have found by hand searching, is NCT00572507.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2013 Jun 22, Rick Gerkin commented:

This is the classic that brought the application of generalized linear models to the understanding of sensory systems into the mainstream.

On 2014 May 15, Bill Sardi commented:

This abstract does not do justice to this landmark study. 12 weeks on an CR diet significantly differentiated 198 genes, resveratrol 225 genes,resveratrol+polyphenol (Longevinex) diet 1711 genes. Life-long CR diet in mice differentiates 831 genes. This suggests what takes a lifetime to accomplish epigenetically can be accomplished over a shorter time! Furthermore, the nutraceutical switched 677 of 831 (82%) longevity genes in the same direction (expressed or silenced) as CR, which makes this nutraceutical the closest molecular mimic of CR to date. (Note: I have a commercial interest in this nutraceutical)

On 2013 Dec 29, Keizo Takao commented:

The raw data of behavioral tests, which are not described in this paper, are shown in the Mouse Phenotype Database (http://www.mouse-phenotype.org/). "ImageLD", "ImageEP", "ImageFZ", and "ImageTM", image analysis application softwares used in this article, are now freely available from http://www.mouse-phenotype.org/software.html.

On 2014 Nov 30, Harri Hemila commented:

Brundage JF, 2008 suggest that the high mortality associated with 1918-19 flu pandemic was caused by secondary bacterial pneumonia. They propose that antibiotics and bacterial vaccines should be stockpiled for the next flu pandemic.

I would like to suggest that as part of pandemic-related research activities, the effect of vitamin C on bacterial pneumonia should be investigated. In dozens of animal studies, vitamin C protected against infections by various viruses and bacteria, see Hemilä 2006, pp. 5-9, 105-21.

In the early 20th century, Alfred Hess carried out extensive studies of scurvy and summarized a large series of autopsy findings as follows: “pneumonia, lobular or lobar, is one of the most frequent complications [of scurvy] and causes of death” and “secondary pneumonias, usually broncho-pneumonic in type, are of common occurrence, and in many [scurvy] epidemics constitute the prevailing cause of death”, see Hemilä H, 2007. Thus, there seemed to be a close association between vitamin C and pneumonia.

Hemilä H, 2013 carried out a Cochrane review, and found 3 controlled trials that looked at whether vitamin C prevents pneumonia and 2 that looked at whether it might help in curing pneumonia; 2 of them were RCTs. Each of the 5 trials found that vitamin C supplementation was beneficial.

As to bacterial pneumonia caused by influenza A infection, Kimbarowski JA, 1967 is particularly relevant as they administered vitamin C to soldiers of the former USSR who were hospitalized because of influenza A. Their main purpose was to examine an investigational laboratory test; but, as a secondary issue, they reported the number of bronchopneumonia cases in the vitamin C and control groups after hospitalization. There were 10 cases of bronchopneumonia in the control group compared with 2 cases in the vitamin C group (P = 0.02, Fisher’s exact test). Although the trial is methodologically unsatisfactory in comparison with current standards, the difference in the occurrence of pneumonia in the study groups cannot be dismissed because of obvious biases Hemilä H, 2013. Methodologically satisfactory trials are needed to corroborate or refute the possibility that vitamin C has an effect on bacterial pneumonia caused by influenza.

On 2016 Aug 24, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT03318019. We believe the correct ID, which we have found by hand searching, is NCT00318019.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2013 Dec 14, Rafael Najmanovich commented:

An online web-tool to detect binding site similarities against a database of non-redundant ligand-protein combinations using IsoCleft has been published here: Kurbatova N, 2013

On 2017 Mar 01, Jörg Hakenberg commented:

GNAT results for Medline citations are now available from our Sourceforge project for download. Please see https://sourceforge.net/projects/gnat/files/results/medline/ .

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT003229758. We believe the correct ID, which we have found by hand searching, is NCT00329758.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2015 Feb 10, Arnaud Chiolero MD PhD commented:

This fantastic paper gives a clear explanation of why it is essential to think about specific interventions to assess the causal effect of overweight on health outcomes.

On 2013 Oct 24, Tom Kindlon commented:

Information on occupational performance does not suggest 37% of the patients were recovered

In a comment,¹ I pointed out that the thresholds for recovery using were set very low. However it is useful to investigate the other data to see what it might say about recovery rates. Also other data from other studies.

This paper claims that CBT led to a recovery rate of 37% in CFS patients. That is one of the highest if not the highest recovery rate I can recall from a CBT study.

It is my impression that, outside the Netherlands, claims of CBT leading to recovery in CFS are not that common. For example, Prof. Simon Wessely, who could be said to be one of the chief proponents of CBT for CFS worldwide over the last couple of decades has said that CBT is not "remotely curative".²

Recently, a meta-analysis of the efficacy of CBT for CFS was published.³ The studies involved a total of 1371 patients. This involved calculating the size of an effect measure, the Cohen's d value.

They calculated d using the following method: "Separate mean effect sizes were calculated for each category of outcome variable (e.g., fatigue self- rating) and for each type of outcome variable (mental, physical, and mixed mental and physical). Studies generally included multiple outcome measures. For all analyses except those that compared different categories or types of outcome variables, we used the mean effect size of all the relevant outcome variables of the study."

d was calculated to be 0.48.

For anyone unfamiliar with Cohen's d values, they are not bounded by 1; also, the higher the score, the bigger the "effect size" i.e. the more "effective" a treatment was found to be. Cohen's d values are considered to be a small effect size at 0.2, a moderate effect size at 0.5, and a large effect size at 0.8.⁴

So this suggests CBT leading to a 37% recovering would be unusual.

I noticed in the discussion section, we are told: "Concerning work productivity, fewer patients had a paid job after treatment than before, but the mean hours of paid work per week had increased after treatment." We are not given the percentage of patients who work.

What we are told is that the median number of hours actually worked before the treatment was 0 hours. That means that at least 50% of the patients were working 0 hours per week. For the whole group, they worked a mean number of 9.4 hours. Some of these people who were not actually working were actually in paid employment at the start as the median number of hours they were contracted to work was 7 hours. Like most studies of adults with CFS, these patients were of working age. The mean age was 38.1 with a standard deviation of 10.2. 34% were male (larger than most studies) and 66% were female.

Given the sample group, one would think that an "effective" treatment for CFS which is supposed to improve functioning and which the authors claim led to a recovery rate of 37% would dramatically effect the professional functioning of the patients.

Indeed at least one study of CBT has used hours worked in its definition of recovery: "Predetermined criteria for "complete recovery" required that patients no longer met chronic fatigue syndrome criteria, were employed full-time, and scored less than 4 on the Fatigue Questionnaire and more than 83 on the Medical Outcomes Study Short-Form General Health Survey physical functioning scale."

However in the current study we find that the mean number of hours work they are contracted to work actually decreased from a mean (SD) of 16.2 (16.3) to 14.9 (16.2) and given that median number of hours actually worked after the intervention is 0 hours, more than 50% of the patients are still not working at the end.

[The number of hours actually worked did increase from a mean (SD) of 9.4 (13.5) to 11.4 (14.7) but a quick t-test suggests this isn't statistically significant].

(contd.)

On 2013 Oct 24, Tom Kindlon commented:

(This follows on from an earlier comment that I thought was already long enough)

Some of the calculations in this study use in the study use the recovery rate of 37%.

For example: "Given the recovery rate of 37% the COR of implementing CBT for CFS was 5.320 per recovered CFS patient. The COR acceptability curve (figure 3) shows that the probability that implementing CBT for CFS has a favorable COR is 100% when the decision maker values a recovered CFS patient at least 6.500."

They become very different if the threshold for recovery was much different.

Alternatively, with the lax definition for recovery they used, the amount of people who would have "recovered" without treatment may be a lot higher than the 5% assumed in this study.

"Finally, to get an impression of this study's results when compensating for spontaneous recovery, an additional analysis was performed. This was done from the health care perspective, presuming a spontaneous recovering rate of 5% [2], implying a recovery rate due to treatment of 32%. It revealed that the COR would rise from 5.320 to about 5.969 per recovered patient."

I also don't understand why a "recovered group" should have a much worse average score in a domain e.g. around the lower end of any scale. Surely the null hypothesis would be that there is no difference between the means and if this is not satisfied, it's not a "recovered group". What could be done would be that some people would have to be taken out of the "recovered group" until one got to a situation where the mean of the recovered group was within a confidence interval for the mean of the normal population (the variance of a mean is of course much smaller than the variance of an individual entry i.e. the average value would have to be "pretty close" to the mean of the healthy population). Of course, it could be the case that there would only be a recovered group of 1 (say) as none of the values were above the mean.

On 2013 Oct 24, Tom Kindlon commented:

Another paper by the authors provides information on the effects of self-efficacy and fatigue severity on health use

I find it strange that in this paper, there is no mention of: "Determinants of health care use in chronic fatigue syndrome patients: a cross-sectional study." That study was written the four authors of the current study plus one other person.¹

I'm far from an expert on health economics but it seems to have information and data relevant to the current study or at least worthy of mention in the discussion section.

They found for example, that: "self-efficacy showed a positive instead of a negative relation with health care use. We checked the direct correlation between self-efficacy and health care use, which also appeared to be positive (Pearson's R=0.12, p=.04). It thus seems that using more health care services might form an aspect of, or is stimulated by, a high self-efficacy instead of being the result of a low self-efficacy."

One of the aims of CBT for CFS is to increase self-efficacy. As they say in the paper: "Subsequently, dysfunctional fatigue related cognitions are being challenged to diminish somatic attributions of fatigue, to improve a sense of control over symptoms and to facilitate behavior change. Finally a plan for work rehabilitation is outlined and worked out. Patients without a paid job focus on rehabilitation in other personal activities. The last session deals with relapse prevention and further improvement of self-control."

Self-efficacy is a commonly used term in the literature on CBT for CFS.

That study¹ also found that: "Fatigue severity itself showed no relation with health care use." So improving fatigue scores will not necessarily change health care use.

References:

[1] Scheeres K, Wensing M, Severens H, Adang E, Bleijenberg G. Determinants of health care use in chronic fatigue syndrome patients: a cross-sectional study. J Psychosom Res. 2008 Jul;65(1):39-46.

On 2014 Jan 08, Tom Kindlon commented:

Thresholds for recovery were set very very "low" (perhaps the bottom percentile and 5th lowest percentile of the healthy adult population on the two scales used)

(I originally posted this as a comment here: http://www.biomedcentral.com/1472-6963/8/175/comments. However all the paragraph breaks have been deleted so I doubt many would read it there)

The thresholds for recovery seem very very low: "Patients were defined as being CSI at post treatment if they had a reliable change index > 1.96 on the CIS fatigue severity subscale [22], a fatigue severity score <= 35 and a Rand-36 physical functioning score > = 65".

Many of the patients already likely had a "physical functioning score >=65" given the mean (SD) values before treatment were: "Physical impairment (Rand 36) 54.0(23.4)" And the threshold for recovery was only 0.47 SDs above the initial mean score. I am aware of the questions on the SF-36 PF subscale (scores can range from 0 to 100 with the higher the score, the better their "physical functionaling") and I don't believe most healthy adults would believe scoring 65 on the SF-36 PF scale would mean they were recovered. As a study[1], that was co-written by one of the authors of this study (Gijs Bleijenberg), pointed out, a community study found that "healthy adults without a chronic condition" had "a mean score of 93.1 (SD 11.7)." The authors of that study[1] pointed out they did not know the exact distribution of the SF-36 subscales - they just made the assumption that the mean - 1SD would represent a threshold for the 85th percentile and rounded this figure to 80.The threshold in the current study is 65. That is 2.4 SDs below the healthy population's mean score. If the same assumptions were made (i.e. that the curve was normally distributed), this would represent the bottom percentile!

For the CIS fatigue severity subscale (where the possible scores are 8-56 with the higher the score, the greater the fatigue), that same study that Gijs Bleijenberg co-wrote[2] used (to calculate thresholds i.e. from another study) a "normal group of 53 healthy adults with a mean age of 37.1 (SD 11.5)" who had "a mean score on the CIS-fatigue of 17.3 (SD 10.1)."[3] The ages of those healthy adults are similar to the ages of the CFS patients in this study: Mean (SD) 38.1 (10.2). In that study[1], they estimated that the 85th percentile (mean+1SD) would be 27 (due to rounding). This study uses 35 or the mean + 1.7525SD or the 95th percentile. Put another way, patients in this study could be considered recovered if they scored in the bottom percentile on the physical functioning subscale (of the SF-36) and in the 5th lowest percentile on the CIS-fatigue scale!

References:

[1] Knoop H, Bleijenberg G, Gielissen MF, van der Meer JW, White PD. Is a full recovery possible after cognitive behavioural therapy for chronic fatigue syndrome? Psychother Psychosom. 2007;76(3):171-6.

[2] Aaronson NK, Muller M, Cohen PD, Essink-Bot ML, Fekkes M, Sanderman R, Sprangers MA, te Velde A, Verrips E: Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease population. J Clin Epidemiol 1998; 51: 1055-1068.

[3] Vercoulen JHMM, Alberts M, Bleijenberg G: De Checklist Individual Strength (CIS) (The Checklist Individual Strength). Gedragstherapie (Behavioural Therapy) 1999; 32: 642-649.

On 2013 Dec 28, Tsuyoshi Miyakawa commented:

"ImageLD", an image analysis appilcation software shown in this video article, is now freely availble from http://www.mouse-phenotype.org/software.html.

On 2014 Nov 30, Harri Hemila commented:

Morens DM, 2008 examined pieces of lung tissues from 1918-19 flu pandemic victims and perused contemporary autopsy reports, concluding that the high mortality associated with influenza was caused by secondary bacterial pneumonia. They suggested that antibiotics and bacterial vaccines should be stockpiled for the next flu pandemic.

I would like to propose that as part of pandemic-related research activities, the effect of vitamin C on bacterial pneumonia should be investigated.

In mice, influenza A infection decreases the level of vitamin C in the lungs Hennet T, 1992, and vitamin C deficiency leads to more severe pathological changes in these organs Li W, 2006. In dozens of animal studies, vitamin C protected against infections by various viruses and bacteria Hemilä 2006, pp. 5-9,105-21. In the early 20th century, Alfred Hess carried out extensive studies of scurvy and summarized a large series of autopsy findings as follows: “pneumonia, lobular or lobar, is one of the most frequent complications [of scurvy] and causes of death” and “secondary pneumonias, usually broncho-pneumonic in type, are of common occurrence, and in many [scurvy] epidemics constitute the prevailing cause of death”, see Hemilä H, 2007. Furthermore, in about two dozen placebo-controlled trials, vitamin C reduced the duration and severity of the common cold suggesting that the vitamin may have effects on the respiratory system of humans even in the absence of frank deficiency Hemilä H, 2013.

Because of the evidence suggesting that vitamin C may have an effect on pneumonia, Hemilä H, 2013 carried out a Cochrane review and found three controlled trials that looked at whether vitamin C prevents pneumonia and two that looked at whether it might help in curing pneumonia. Each of the five trials found that vitamin C supplementation was beneficial. Two of the studies were double-blind placebo-controlled RCTs, one was prophylactic and the other therapeutic.

Pitt HA, 1979 administered vitamin C to US marine recruits and reported 7 cases of pneumonia in the placebo-group compared with 1 case in the vitamin C group. Hunt C, 1994 administered vitamin C to elderly people who were admitted to hospital in the UK because of bronchopneumonia or acute exacerbation of chronic bronchitis. They reported a significant decrease in the “total respiratory score” by vitamin C administration, and 5 deaths in the placebo group compared with 1 death in the vitamin C group. Hunt et al. tested the effect of vitamin C “over and above those of normal medication (mainly antibiotics and cough medicines) to which all participants were exposed” so that all their patients received antibiotics and vitamin C was not an alternative to them.

As to bacterial pneumonia caused by influenza A infection, Kimbarowski JA, 1967 is particularly interesting as they administered vitamin C to soldiers of the former USSR who were hospitalized because of influenza A. Their main purpose was to examine an investigational laboratory test; however, as a secondary issue, they reported the number of bronchopneumonia cases in the study groups after hospitalization. The reason for diagnosing pneumonia was that the authors excluded those cases from their further study of the laboratory test. Thus, the pneumonia cases occurred after vitamin C supplementation was initiated for the influenza A patients. The two arms were balanced for the severity of influenza. The allocation method was not described but the study arms were of very similar size (112 versus 114 in the control and vitamin C arms) so it is possible that allocation occurred sequentially in the two trial arms. A placebo was not mentioned in the paper and apparently not used. Blinding of outcome assessment was not described; however, since pneumonia was a nuisance issue in their study, it seems improbable that the trial authors had substantial bias in their diagnosis of pneumonia. There were 10 cases of bronchopneumonia in the control group compared with 2 cases in the vitamin C group (P = .02, Fisher’s exact test).

Even though Kimbarowski and Mokrow’s trial is methodologically unsatisfactory in comparison with current standards, the difference in the occurrence of pneumonia in the study groups cannot be dismissed because of obvious biases Hemilä H, 2013. Furthermore, the finding is consistent with other evidence suggesting that under some conditions vitamin C may affect respiratory infections. Methodologically satisfactory trials are needed to corroborate or refute the possibility that vitamin C has an effect on bacterial pneumonia caused by influenza.

On 2015 Aug 05, Tracy Shields commented:

According to a retraction statement published online 2 July 2014, the article was retracted "due to errors in the data analysis which affect the article's findings" by agreement between the journal Editor-in-Chief, the authors, and publisher. See: http://onlinelibrary.wiley.com/doi/10.1111/jocn.12652/abstract

On 2013 Dec 29, Keizo Takao commented:

The raw data of behavioral tests, which are not described in this paper, are shown in the Mouse Phenotype Database (http://www.mouse-phenotype.org/). "ImageFZ", image analysis application software used in this article, is now freely available from http://www.mouse-phenotype.org/software.html.

On 2016 Oct 03, Morten Oksvold commented:

Please note that after an investigation at the University of Cologne, six articles where T. Wenz figures as first or senior author were found to contain questionable data due to scientific misconduct. This article is one of these six articles.

The conclusion from the report was ready June 28, 2016, please see the link (in German):

http://www.portal.uni-koeln.de/9015.html?&tx_news_pi1[news]=4335&tx_news_pi1[controller]=News&tx_news_pi1[action]=detail&cHash=1deb8399d7f796d65ca9f6ae4764a1ce

On 2014 Jan 08, Brett Snodgrass commented:

Dear Authors,

Thank you for the excellent article. Please provide your kind consideration to the vascular nomenclature and the distinction between the Thebesian veins and the vessels of Wearn.

Might the article be titled as the "Wearn Coronary System?" The fine (minute) nature of the vessels is consistent with the arteriosinusoidal type of vessels of Wearn. However, arterio-capillary-cameral & arterio-capillary-venular-cameral connections might be considered.

The text highlights found at the following website may illustrate the difference.

https://twitter.com/BrettSnodgrass1/status/417972133642240000/

Comments and suggestions are welcome.

Thank you kindly.

On 2017 May 18, Misha Koksharov commented:

FYI:

In contrast to Luciola mingrelica luciferase, in Photinus pyralis luc the homologous mutation Y33H doesn't affect pH-sensitivity of its color (at least there are no any differences in E. coli colonies compared with WT Ppy). Apparently, something is different in the surrounding interactions (it's not particularly surprising given the 67% sequence identity between the P. pyralis and L. mingrelica luciferases).

Regarding this region of the 3D structure, the mutation D234G in Ppy luc comes to mind: it makes Ppy luc quite pH-resistant (hence, greenish colony color with low pH-dependent shift); however, in the fully pH-sensitive L. mingrelica luc the corresponding residue is already G236.

On 2014 Jan 27, Anders von Heijne commented:

Development of visual diagnostic skills in pathology, as in radiology, is a sine qua non to become proficient in ones field. It requires training, feedback and guidance by more experienced collegues. It would be interesting if the author would like to update readers how the changes in the educational system actually impacted training and professional development, now several years after the original paper.

On 2014 Jan 22, Ernesto Guiraldes commented:

This is an interesting report. I would suggest that the fact that the patient was eventually diagnosed as having Cystic fibrosis be made clear in the abstract.

On 2014 Mar 18, Jacob Blumenthal commented:

Hi, as the corresponding author of this paper, I will be happy to receive any of your comments. Please contact me via my current e-mail: jacob.blu@gmail.com or visit my site Stem Cells and Tissue Engineering.

On 2013 Dec 29, Keizo Takao commented:

The raw data of behavioral tests, which are not described in this paper, are shown in the Mouse Phenotype Database (http://www.mouse-phenotype.org/). "ImageLD", "ImageEP", and "ImageFZ", image analysis application softwares used in this article, are now freely available from http://www.mouse-phenotype.org/software.html.

On 2013 Dec 29, Keizo Takao commented:

The raw data of behavioral tests, which are not described in this paper, are shown in the Mouse Phenotype Database (http://www.mouse-phenotype.org/). "ImageEP", "ImageLD" and "ImageFZ", image analysis application softwares used in this article, are now freely available from http://www.mouse-phenotype.org/software.html.

On 2015 Jul 27, Bill Cayley commented:

A good example of when "less" is "more": https://lessismoreebm.wordpress.com/2015/07/27/diagnostic-accuracy-of-an-initial-azoospermic-reading-compared-with-results-of-post-centrifugation-semen-analysis-after-vasectomy/

On 2013 Oct 20, Ivan Oransky commented:

This study has been retracted. Here's background from Retraction Watch (which I co-founded): http://retractionwatch.wordpress.com/2013/10/09/retraction-appears-for-stem-cell-researcher-found-to-have-used-funds-for-his-companys-gain/

And here is corresponding author Gerold Feuer's response to the retraction, in which he says he can "unequivocally state that the data in all published manuscripts is valid and sound and is not falsified or fabricated": http://retractionwatch.wordpress.com/2013/10/11/stem-cell-scientist-says-data-in-retracted-paper-is-not-falsified-or-fabricated/

On 2014 Aug 29, Massimo Ciccozzi commented:

I read with interest the paper by Santosh et al. HIV 2 infections were geographically restricted, affecting West African countries1, 2, whereas in Europe the prevalence of HIV-2 is high in those countries that have socioeconomic relationships with this African region 3-5 . However, increased migration from/to other countries and international travels might increase the risk of spreading of this virus worldwide. We recently published a phylogenetic analysis of HIV2 case series in Italy using sequences isolated from Indian patients try to connect this infection with other from different countries 6. This article is very interesting and give important information on HIV-2 using a complete genome analysis. In Indian country few papers have been written in Phylogenetic analysis and Santosh and coauthors have given a sort of thrust in this way. In my opinion I only suggest the use of Bayesian Methods to have more robust information about the origin of Indian epidemics, moreover I encourage all Indian researchers, that are able to make sequences, to do this. It is also important that the scientific community don't lower the watch and monitor this infection also because to study HIV 2 can be an opportunity to better understand the HIV disease pathogenesis, protein immunity and this have to be taken before it disappears.<br> References

1. Dougan S, Patel B, Tosswill JH, and Sinka K: Diagnoses of HIV-1 and HIV-2 in England, Wales, and Northern Ireland associated with west Africa. Sex Transm Infect 2005;81:338– 341.

2. Matheron S, Mendoza-Sassi G, Simon F, Olivares R, Coulaud JP, and Brun-Vezinet F: HIV-1 and HIV-2 AIDS in African patients living in Paris. AIDS 1997;11:934–936.3

3. Valadas E, Franc¸a L, Sousa S, and Antunes F: 20 years of HIV-2 infection in Portugal: Trends and changes in epidemiology. Clin Infect Dis 2009;48:1166–1167.

4. Barin F, Cazein F, Lot F, et al.: Prevalence of HIV-2 and HIV-1 group O infections among new HIV diagnoses in France: 2003– 2006. AIDS 2007;21:2351–2353.

5. Soriano V, Gomes P, Heneine W, et al.: Human immunodeficiency virus type 2 (HIV-2) in Portugal: Clinical spectrum, circulating subtypes, virus isolation, and plasma viral load. J Med Virol 2000;61:111–116.

6. D’Ettorre,G, Lo Presti A,Gori C, Cella E,Bertoli A,Vullo V,Perno CF, Ciotti M,. Foley Brian T, and Massimo Ciccozzi. An HIV Type 2 Case Series in Italy: A Phylogenetic Analysis.(2013) AIDS Res HUM Retroviruses

On 2013 Oct 03, Brett D Thombs commented:

The authors recommend routine depression screening for all heart patients. Even in primary care, where physicians are trained to manage depression, this is not recommended. This recommendation by the AHA was not based on a systematic review of evidence of benefit. See two systematic reviews:

Thombs BD, de Jonge P, Coyne JC, Whooley MA, Frasure-Smith N, Mitchell AJ, Zuidersma M, Eze-Nliam C, Bezerra B, Smith CG, Soderlund K, Ziegelstein RC. Depression screening and patient outcomes in cardiovascular care: A systematic review. JAMA. 2008;300(18):2161-2171.

Thombs BD, Roseman M, Coyne JC, de Jonge P, Delisle VC, Arthurs E, Levis B, Ziegelstein R. Does Evidence Support the American Heart Association's Recommendation to Screen Patients for Depression in Cardiovascular Care? An Updated Systematic Review. PLoS ONE. 2013;8(1):e52654.

On 2013 Oct 24, Tom Kindlon commented:

Data subsequently releasted from on this study reveals that there was no change in activity levels

Some people may be interested to know that a review of three Dutch Chronic Fatigue Syndrome (CFS) studies that was subsequently released¹ showed that this intervention did not result in an increase in physical activity levels in this study² along with two other Dutch CBT studies ^3,4. The authors say these three studies were based on the same general therapeutic approach to the illness.⁵

The mean (standard deviations) for the guided self-instructions/CBT and Control groups were respectively 63.1 (23.5) and 63.5 (21.8) before treatment and 67.3 (22.5) and 67.8 (21.4) at the second assessment. In terms of change scores, this equates to: 4.3 (20.4) and 4.3 (21.0). Different devices can be used to measure activity levels; for the actometers used in this study, healthy controls were previously found to have a mean Actometer score of 91 (S.D.=25).⁶ That study found that the mean Actometer score of tested CFS patients was 66 (S.D.=22).⁶

Another research team in the US also found similar results with regard to physical activity.⁷ In a study investigating an intervention involving Cognitive Behavior Therapy (CBT) which included encouraging CFS patients for going for longer walks, they found that on the SF-36 Physical Functioning (PF) scale, patients improved from a pre-treatment mean (SD) of 49.44 (25.19) to 58.18 (26.48) post-treatment, equivalent to a Cohen's d value of 0.35. On the Fatigue Severity Scale (FSS), the improvement as measured by the cohen's d value was even great (0.78) from an initial pre-treatment mean (SD) of 5.93 (0.93) to a 5.20 (0.95) post-treatment. However on actigraphy there was actually a numerical decrease from a pre-treatment mean (SD) of 224696.90 (158389.64) to 203916.67 (122585.92) post-treatment (cohen's d: -0.13).

These studies raise questions about what are the best outcome measures to use in trials of CBT for CFS.

References:

[1] Wiborg JF, Knoop H, Stulemeijer M, Prins JB, Bleijenberg G. How does cognitive behaviour therapy reduce fatigue in patients with chronic fatigue syndrome? The role of physical activity. Psychol Med. 2010 Jan 5:1-7. [Epub ahead of print]

[2] Knoop H, van der Meer JW, Bleijenberg G (2008). Guided self-instructions for people with chronic fatigue syndrome: randomised controlled trial. British Journal of Psychiatry 193, 340-341.

[3] Stulemeijer M, de Jong LW, Fiselier TJ, Hoogveld SW, Bleijenberg G (2005). Cognitive behaviour therapy for adolescents with chronic fatigue syndrome: randomised controlled trial. British Medical Journal 330. Published online : 7 December 2004. doi:10.1136/bmj.38301.587106.63.

[4] Prins JB, Bleijenberg G, Bazelmans E, Elving LD, de Boo TM, Severens JL, van der Wilt GJ, Spinhoven P, van der Meer JW (2001). Cognitive behaviour therapy for chronic fatigue syndrome: a multicentre randomised controlled trial. Lancet 357, 841-847.

[5] Bleijenberg G, Prins JB, Bazelmans E (2003). Cognitive behavioral therapies. In Handbook of Chronic Fatigue Syndrome (ed. L. A. Jason, P. A. Fennell and R. R. Taylor), pp. 493-526. Wiley: New York.

[6] Van der Werf SP, Prins JB, Vercoulen JH, van der Meer JW, Bleijenberg G (2000). Identifying physical activity patterns in chronic fatigue syndrome using actigraphic assessment. Journal of Psychosomatic Research 49, 373-379.

[7] Friedberg F, Sohl S. Cognitive-behavior therapy in chronic fatigue syndrome: is improvement related to increased physical activity? J Clin Psychol. 2009 Feb 11.

On 2013 Oct 24, Tom Kindlon commented:

Are there CBT studies with CFS controls that found objective (e.g. actometer) increases in activity?

In an e-letter,¹ I pointed out the problem of the lack of use and/or reporting of objective outcome measures in the area.

I have read more papers on the topic since then and it is interesting that the Nijmegen group themselves have been aware of this issue for over a decade. For example, two of the three co-authors of this study co-wrote a paper in 1997² which said: "It is not clear whether subjective accounts of physical activity level adequately reflect the actual level of physical activity. Therefore the primary aims of the present study were to assess actual activity level in patients with CFS to validate claims of lower levels of physical activity and to validate the reported relationship between fatigue and activity level that was found on self-report questionnaires. In addition, we evaluated whether physical activity level adequately can be assessed by self-report measures. An Accelerometer was used as a reference for actual level of physical activity.". The authors reported on the correlations on 7 outcome measures in relation to the actometer readings: "none of the self-report questionnaires had strong correlations with the Actometer. Thus, self-report questionnaires are no perfect parallel tests for the Actometer." The authors pointed out that "the subjective instruments do not measure actual behaviour. Responses on these instruments appear to be an expression of the patients' views about activity and may be biased by cognitions concerning illness and disability."

This finding was re-iterated in another paper three years later again involving Bleijenberg and Van der Meer:³ "In earlier studies of our research group, actual motor activity has been recorded with an ankle-worn motion-sensing device (actometer) in conjunction with self-report measures of physical activity. The data of these studies suggest that self-report measures of activity reflect the patients' view about their physical activity and may have been biased by cognitions concerning illness and disability."

So could it be the case that Cognitive Behaviour Therapy for CFS is simply changing how patients respond to self-report questionnaires and that no actual changes of activity are occurring? Some research suggests this is possible.⁴

References

1 Kindlon T. How effective is the treatment for CFS? http://bjp.rcpsych.org/content/193/4/340.abstract/reply#bjprcpsych_el_22380

2 Vercoulen JH, Bazelmans E, Swanink CM, Fennis JF, Galama JM, Jongen PJ, Hommes O, Van der Meer JW, Bleijenberg G. Physical activity in chronic fatigue syndrome: assessment and its role in fatigue. J Psychiatr Res. 1997 Nov-Dec;31(6):661-73.

3 van der Werf SP, Prins JB, Vercoulen JH, van der Meer JW, Bleijenberg G. Identifying physical activity patterns in chronic fatigue syndrome using actigraphic assessment. J Psychosom Res. 2000 Nov;49(5):373-9.

4 Wiborg JF, Knoop H, Stulemeijer M, Prins JB, Bleijenberg G. How does cognitive behaviour therapy reduce fatigue in patients with chronic fatigue syndrome? The role of physical activity. Psychol Med. 2010 Aug;40(8):1281-7.

On 2013 Oct 24, Tom Kindlon commented:

Treating severely affected CFS patients with CBT

This study found that the "more severely disabled patients benefit less from the self-instructions" (to be more specific, "the treatment effect is more than halved for patients with an SIP8 score of 1 standard deviation above the mean").

At least one other Cognitive Behavioural Therapy (CBT) for Chronic Fatigue Syndrome (CFS) study found that the outcome may be related to the level of impairment at baseline[1]. In this study, 10 (37%) reported being "better or much better" following treatment and 17 (63%) were the same or worse (we are not given a breakdown between the two categories).

Comparing the two groups at baseline, the patients who reported improvement reported significantly less (p<0.05) functional impairment as measured by the SIP-8 [a mean (SD) of 1330 (417) vs 1985 (730)], less daily observed fatigue [7.4 (2.6) vs 9.7 (2.3)], and less daily observed pain [4.5 (2.6) vs 7.8 (3.5)]. For the pre-treatment variable "mean hours working a week" a trend (p=0.062) was found with improved patients working more hours at baseline compared to non-improved patients [10.9 (12.8) vs 2.6 (6.6)]. As with the current study, there was not a statistical difference on initial fatigue (CIS-fatigue). The paper¹ devotes quite a bit of space to this analysis including a table (Table 3).

Reference:

1 Bazelmans E, Prins JB, Lulofs R, van der Meer JW, Bleijenberg G. Cognitive behaviour group therapy for chronic fatigue syndrome: a non-randomised waiting list controlled study. Psychother Psychosom. 2005;74(4):218-24

On 2017 Jul 07, Morten Oksvold commented:

This article should have been retracted after an investigation by The University of Maryland found this article to contain "compromised" data (a total of 26 articles in 11 journals were affected). The journal Cancer Research was informed in August 2016, according to Retraction Watch.

http://retractionwatch.com/2017/04/26/university-asked-numerous-retractions-eight-months-later-three-journals-done-nothing/

On 2014 Apr 06, Shervin Assari commented:

One of the unique contributions by Professor Shemshadi to a field with limited studies.

On 2014 Jan 19, Kamil Brzóska commented:

Current e-mail address of the corresponding author: k.brzoska@ichtj.waw.pl

On 2014 Mar 07, Devi Prasad Mohapatra commented:

This is a very interesting and practical article. Highly recommended for all cleft surgeons. What remains to be seen is how important is the experience of the operating surgeon and technique of palatoplasty in contributing to the formation of palatal fistula. Would the authors possibly throw some light on that?

On 2013 Nov 05, Pedro Mendes commented:

This yeast metabolic reconstruction has since been further improved by Dobson PD, 2010, Heavner BD, 2012 and Heavner BD, 2013. The up to date data set is available from http://yeast.sf.net/.

On 2014 Jan 08, Tom Kindlon commented:

The references don't show that CFS "affects at least 4 million adults in the United States"

This is not necessarily a major point to do with the methodology. However given the paper involves CFS medical education, and gives few facts about CFS, one would think that the statements that are made are at least reasonably accurate.

However, this statement clearly isn't: "CFS affects at least 4 million adults in the United States [2-4]." The references given are the first three references below. The second study found a prevalence of 422 per 100,000 adults and the third study found a prevalence of 235 per 100,000 adults. These aren't even close to 4 million adults - the second one suggests a figure of at least 400,000 adults. This study involved one of the authors (William C Reeves). The other study[1], also involved Reeves, did find a prevalence of 2540 per 100,000 adults, which would equate to over 4 million adults. As can be seen, this is much higher than previous estimates of the prevalence of CFS in the US. The reason for the huge discrepancy is largely because it used a different method of defining CFS[4].

There has been some criticism of this new definition[5]. Unlike previous times when the CDC produced definitions for CFS[6,7], the definition used in this study is generally only being used by the CDC-funded CFS research team [the cohorts from the Wichita 2-day study in 2003 (not to be confused with the Reyes study) and the Georgia prevalence study[3]]. So it's far from clear that most people would accept that CFS "affects at least 4 million adults in the United States". But certainly two of the three studies given to back up this reference did not find anything close to such a prevalence.

References:

[1] Reeves WC, et al. Prevalence of chronic fatigue syndrome in metropolitan, urban, and rural Georgia. Population Health Metrics 2007, 5:5.

[2] Jason LA, Richman JA, Rademaker AW, Jordan KM, Plioplys AV, Taylor RR, McCready W, Huan CF, Plioplys S: A community-based study of chronic fatigue syndrome. Arch Int Med 1999, 159:2129-2137.

[3] Reyes M, Nisenbaum R, Hoaglin DC, Emmons C, Stewart G, Randall B, Stewart JA, Abbey S, Jones JF, Gantz N, Minden S, Reeves WC: Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas. Arch Intern Med 2003, 163:1530-1536.

[4] Reeves WC, Wagner D, Nisenbaum R, Jones JF, Gurbaxani B, Solomon L, Papanicolaou DA, Unger ER, Vernon SD, Heim C: Chronic Fatigue Syndrome – A clinically empirical approach to its definition and study. BMC Medicine 2005, 3:19 (15 December 2005)

[5] Jason LA, Richman JA: How Science Can Stigmatize: The Case of Chronic Fatigue Syndrome. Journal of Chronic Fatigue Syndrome, Vol. 14(4), 2007

[6] Fukuda, K., Straus, S.E., Hickie, I., Sharpe, M.C., Dobbins, J.G., & Komaroff, A. (1994). The chronic fatigue syndrome: A comprehensive approach to its definition and study. Annals of Internal Medicine, 121 (12):953-959. http://www.annals.org/cgi/content/full/121/12/953

[7] Holmes GP, Kaplan JE, Gantz NM, Komaroff AL, Schonberger LB, Straus SE, et al. Chronic fatigue syndrome: a working case definition. Ann Intern Med. 1988; 108:387-9.

On 2014 Jul 02, Balázs Győrffy commented:

We have recently (2014) summarized an up-to-date version of EOC biomarkers: http://www.ncbi.nlm.nih.gov/pubmed/18926090. Drop a mail and we will be happy to share our paper: penzvaltozsofi@gmail.com

On 2013 Nov 09, Jeffrey Beall commented:

Much of the text in this article matches word-for-word the text in an earlier-published article. The earlier article is: Tanaka E.. Clinically important pharmacokinetic drug-drug interactions: role of cytochrome P450 enzymes. J Clin Pharm Ther. 1998 Dec;23(6):403-16.

PMID: 10048501

On 2013 Dec 29, Keizo Takao commented:

The URL of the gene-brain-phenotyping database has now changed to http://www.mouse-phenotype.org/ (the Mouse Phenotype Database). "ImageLD", "ImageEP", and "ImageTM", image analysis application softwares used in this article, are now freely available from http://www.mouse-phenotype.org/software.html.

On 2017 Jan 23, Christopher Southan commented:

IUPHAR-DB is now subsumed into a new resource. For the latest description see "The IUPHAR/BPS Guide to PHARMACOLOGY in 2016" https://www.ncbi.nlm.nih.gov/pubmed/26464438

On 2016 Mar 29, Jonathan Wren commented:

Apparently relocated to: https://rrndb.umms.med.umich.edu/search/

On 2015 Aug 26, Bill Cayley commented:

A good example of when "less" is "more": https://lessismoreebm.wordpress.com/2015/08/26/fasting-and-nonfasting-lipid-levels-influence-of-normal-food-intake-on-lipids-lipoproteins-apolipoproteins-and-cardiovascular-risk-prediction/

On 2013 Dec 30, Keizo Takao commented:

The raw data of behavioral tests, which are not described in this paper, are shown in the Mouse Phenotype Database (http://www.mouse-phenotype.org/). "ImageLD", "ImageEP", "ImageTM", and "ImageFZ", image analysis application softwares used in this article, are now freely available from http://www.mouse-phenotype.org/software.html.

On 2016 Mar 18, ZHONGMING ZHAO commented:

My lab recently moved to the University of Texas Health Science Center at Houston. The web site for this database is now available at https://bioinfo.uth.edu/ERGR/.

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT00211459. We believe the correct ID, which we have found by hand searching, is NCT00121459.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2014 May 23, Swapnil Rane commented:

I am skeptical about the diagnosis. Based on the histological features and immunohistochemical profile reported in this article, I would categorize the tumor as a malignant mesothelioma instead of adnexal tumor of wolffian origin.

On 2016 Sep 22, Christopher Sampson commented:

This study purports to evaluate biannual (twice a year) screening. However - though it is not entirely clear from the manuscript - the results suggest that it actually evaluates biennial (every 2 years) screening.

On 2014 Aug 25, M Mangan commented:

There's a report that this resource is no longer available: https://www.biostars.org/p/110420/

Can the authors offer any guidance on this?

On 2016 Mar 17, Jonathan Wren commented:

It looks like it's been relocated to: http://c1.accurascience.com/miRecords/

On 2017 Sep 21, Eric Yarnell commented:

This paper is absurd. What possible reason could there be to show that a dose 10,000+ times the normal dose is toxic? Isn't that a given with all known substances? I fear many people would read the abstract or even just the title and conclude that Hibiscus sabdariffa, which is incredibly safe, is dangerous, while in fact this paper supports just how safe it is (when given in reasonable amounts).

On 2016 Nov 02, ALAN HOWE commented:

This is an interesting & cleverly executed paper. It dovetails well with our recent observations of spatial heterogeneity of ATP as well as ATP:ADP ratio in migrating cells (PMID 27385336). Unfortunately, we didn't come across this paper until now; otherwise, we certainly would've cited it.

On 2014 Mar 28, Francois Cachat commented:

Excellent article, explaining the value of ROC curve and influence of disease prevalence. Page 416, positive predictive value ratio should read a/(a+c) and not a/(a+b). Numbers and % are correct.

On 2014 Jan 14, Sergei Jargin commented:

More details: Jargin SV. Barriers to the importation of medical products to Russia: in search of solutions. Healthcare in Low-resource Settings 2013;1:e13. http://www.pagepressjournals.org/index.php/hls/article/view/728

On 2016 Aug 23, Ben Goldacre commented:

This trial has the wrong trial registry ID associated with it on PubMed: both in the XML on PubMed, and in the originating journal article. The ID given is NCT0061850. We believe the correct ID, which we have found by hand searching, is NCT00618150.

This comment is being posted as part of the OpenTrials.net project^[1] , an open database threading together all publicly accessible documents and data on each trial, globally. In the course of creating the database, and matching documents and data sources about trials from different locations, we have identified various anomalies in datasets such as PubMed, and in published papers. Alongside documenting the prevalence of problems, we are also attempting to correct these errors and anomalies wherever possible, by feeding back to the originators. We have corrected this data in the OpenTrials.net database; we hope that this trial’s text and metadata can also be corrected at source, in PubMed and in the accompanying paper.

Many thanks,

Jessica Fleminger, Ben Goldacre*

[1] Goldacre, B., Gray, J., 2016. OpenTrials: towards a collaborative open database of all available information on all clinical trials. Trials 17. doi:10.1186/s13063-016-1290-8 PMID: 27056367

* Dr Ben Goldacre BA MA MSc MBBS MRCPsych<br> Senior Clinical Research Fellow<br> ben.goldacre@phc.ox.ac.uk<br> www.ebmDataLab.net<br> Centre for Evidence Based Medicine<br> Department of Primary Care Health Sciences<br> University of Oxford<br> Radcliffe Observatory Quarter<br> Woodstock Road<br> Oxford OX2 6GG

On 2014 Jun 01, Ivan Oransky commented:

This paper was recently retracted: http://retractionwatch.com/2014/05/28/diabetes-researcher-who-says-he-will-no-longer-publish-now-up-to-five-retractions/ It's the fifth retraction for Cory Toth.

On 2016 Nov 01, Michael Axtell commented:

CleaveLand is no longer available at the URL given in this paper. Instead, the most current release of CleaveLand can be found at github at https://github.com/MikeAxtell/CleaveLand4/releases

Full documentation and a tutorial for CleaveLand4 can be found at https://psu.box.com/v/axtelldata , in directory 'CleaveLand4_Tutorial'.

Best Wishes, Mike Axtell

On 2017 Feb 08, Stuart RAY commented:

Additional (2016) context for this discovery is provided here: Sotiriou MC, 2016.

On 2013 Jun 17, Mike Fay commented:

This article gives a nice Bayesian argument for using three-sided tests for comparing two treatments. So after running a three-sided test for testing between treatments A and B, we conclude either (1) A is better than B, (2) B is better than A, or (3) the data are not sufficient to say which is better. This makes sense to me, since we usually want to know which is better when we reject the null that they are the same.

For standard use, this essentially translates into using two one-sided tests at the 0.025 level.

Another advantage of shifting to three-sided tests (or two one-sided 0.025 level tests), is that it avoids absurd things such as rejecting a two-sided hypothesis test, but then failing to reject after adding more data in either direction. See Vos and Hudson (2008) http://onlinelibrary.wiley.com/doi/10.1111/j.1467-842X.2007.00501.x/abstract for examples.

On 2016 Nov 12, Daniel Corcos commented:

It is surprising that the prevalent screen of the control group detects 14% more cancers than that of the screened group. Also, the incidence of cancer at the second screen is much too high as compared to cancer incidence in the control group. Both results strongly suggest that cancers ascribed to the second round of screening were actually detected at the prevalent screen of the screened group. After correction, it appears that all the differential increase between screened and control groups occurs between the second and third round of screening. Therefore we are not dealing with cancers that spontaneously regress, but with cancers that are induced by x-rays.

On 2014 Aug 24, Ivan Oransky commented:

This study has been retracted: http://retractionwatch.com/2014/08/21/second-study-of-widely-touted-cancer-and-hiv-cure-retracted/ It's one of two recent retractions by the group: http://retractionwatch.com/2014/07/25/paper-about-widely-touted-but-unapproved-cure-for-cancer-autism-retracted/

On 2016 Jan 24, Daniel Schwartz commented:

This is a useful index that may offer a non-invasive approach in patients with liver disease. In practice, clinicians often have difficulty calculating this at the bedside. Here is a simple to use web-based and smartphone-based tool to calculate the APRI

http://www.qxmd.com/calculate/apri-ast-to-platelet-ratio-index

Conflict of interest: Medical Director, QxMD

On 2013 Nov 15, Darren L Dahly commented:

In figures 1 and 2, the arrows should be pointing from latent AFA and latent HT to (not from) AFA and HT, respectively, i.e. the observed indicators are determined by the underlying latent variable, not the other way around. This graphical typo also explains why there are no disturbance terms displayed for latent AFA and latent HT. They are in fact exogenous, and all reported results in the paper reflect the correctly specified model.

On 2014 Feb 28, Uwe Kornak commented:

Another study (Cui et a. Nat Med. 2011 Jun;17(6):684-91) casts severe doubts on the results and conclusions of this publication.

On 2015 Jul 30, Keith Bradnam commented:

As of 2015, development of CEGMA v2 has ceased completely and we can no longer respond to support requests. While the latest version still runs, it should be pointed out that the underlying set of orthologs that CEGMA uses is based on the KOGs database that was published back in 2003. We would advise potential users of CEGMA to consider newer tools like BUSCO which perform a similar role, are easier to install, take less time to run, and which are based on more modern sets of orthologous genes. We don't rule out making a completely new version of CEGMA some day, but for now we consider CEGMA v2 an end-of-life product.

On 2014 Aug 16, Christopher Southan commented:

A 2013 study of BACE evolution http://www.ncbi.nlm.nih.gov/pubmed/24381583 (the " A tale of two drug targets" citation link on the right) indicates the fly beta-secretase-like enzyme refered to above (UniProt Q9VLK3) is not in fact BACE-like but rather a lysosomal cathepsin. By the criteria detailed in that paper, true BACE-like homologs are absent from Drosophila and possibly all Ecdysozoa

On 2015 Nov 29, Mauro De Rosa commented:

Replacing branded medicines with generic ones

In most countries, regulatory agencies have the responsibility of indicating the situations in which branded medicines can be substituted by equivalent ones (i.e. generics). In Italy, these regulatory recommendations have been issued by our national medicine agency (AIFA) by indicating the exceptions, i.e. those cases where a branded medicine cannot be replaced by the equivalent counterpart. A total of six such cases have been identified in Italy, which included the following agents: levetiracetam and topiramate (1), levotyroxine (2), cyclosporine (3), metformine + glibenclamide (4), tacrolimus (5).

References

1. AIFA website - Specialità medicinali contenenti Levetiracetam e Topiramato (17/09/2012), http://www.agenziafarmaco.gov.it/it/content/specialità-medicinali-contenenti-levetiracetam-e-topiramato-17092012); accessed 3.9.2015

2. AIFA website - http://www.agenziafarmaco.gov.it/it/content/precisazione-aifa-sulla-prescrizione-base-di-levotiroxina-15122014 accessed 30.10.2015

3. AIFA website - http://www.agenziafarmaco.gov.it/it/content/precisazioni-aifa-su-specialità-medicinali-contenenti-ciclosporina-15042015 Precisazioni AIFA del 15 aprile 2015 su specialità medicinali contenenti ciclosporina accessed 3.9.2015

4. AIFA website http://www.agenziafarmaco.gov.it/it/content/liste-di-trasparenza-metformina-e-glibenclamide-500-mg-5-mg accessed 3.9.2015

5. AIFA website http://www.agenziafarmaco.gov.it/it/content/precisazioni-aifa-su-specialità-medicinali-contenenti-tacrolimus accessed 3.9.2015

On 2014 Sep 24, Michael Schroda commented:

This system works extremely well and represents an outstanding improvement of the Chlamydomonas toolkit.

On 2015 Jun 29, Bill Cayley commented:

A good example of when Less is More: https://lessismoreebm.wordpress.com/2015/06/29/efficacy-and-safety-of-steroid-injections-for-shoulder-and-elbow-tendonitis-a-meta-analysis-of-randomised-controlled-trials/

On 2014 Mar 05, Jacob Puliyel commented:

IT IS EXPEDIENT BUT IS IT PRUDENT TO LABEL ADVERSE EVENTS FOLLOWING IMMUNIZATION AS 'NOT AN EVENT OF [AEFI]'?

The old scheme of monitoring signals for vaccine safety (adverse events following immunization – AEFI monitoring), of the Advisory Committee on Causality Assessment Collet JP, 2000 has been overtaken by the Revised WHO Classification of AEFI. The changes have been described in 4 PubMed articles Tozzi AE, 2013, Bonhoeffer J, 2009, Halsey NA, 2012, Williams SE, 2013.

I wrote two very detailed comments to the article by Tozzi et al Tozzi AE, 2013 on the PubMed Commons which is envisaged as a forum for open constructive criticism and discussion of scientific issues. To facilitate meaningful discussion it has a link to 'Invite an author to comment'. Tozzi and colleagues have not responded so far. PubMed suggests that the main reason for not getting a response is a changed email contact address.

As this is a matter of patient safety I think it is important that the experts who understand the new scheme must explain why the revision was needed and that it is an improvement over the old scheme - that it will not miss opportunities of picking up new signals by classifying AEFI as 'Not a case of [AEFI]'. I will not repeat the posting but it may be viewed here

The purpose of this posting is to invite the learned authors of this article on causality assessment Bonhoeffer J, 2009 to respond. The article by Bonhoeffer and colleagues mostly describes a guideline for collection of data which is unexceptional, but the subsequent 'analysis and presentation of vaccine safety data in the surveillance system' may cause signals to be ignored because they are classified as ‘Not a case of [AEFI]’. Would the new scheme have picked up and flagged signals of adverse-effects like the RotaShield-reactions, had the scheme been in use in 1998?

On 2017 May 12, Kevin Hall commented:

In the copy editing process, it appears that several typographical errors were introduced in equations 5, 10, 14, and 15. The corrected equation 5 is:

DeltaBW = [(1-beta) x DeltaEI -Deltadelta x BWinit -(gammaFFM -gammaFM) x FM_init]/(gammaFFM +deltainit +Deltadelta) +[C x (gammaFFM -gammaFM)/(gammaFM +deltainit +Deltadelta)] x LambertW{[(gammaFM +deltainit+Deltadelta) x FMinit]/[C x (gammaFFM +deltainit +Deltadelta)] x Exp[[(gammaFM +deltainit +Deltadelta) x FMinit]/[C x (gammaFFM +deltainit +Deltadelta)]] x Exp[[(1-beta) x DeltaEI - Deltadelta x BWinit]//[C x (gammaFFM +deltainit +Deltadelta)]]}

The corrected Equation 10 is:

DeltaEI = Deltadelta x BWinit/(1-beta) + C² x (gammaFFM-gammaFM) x FMinit/(1-beta) + DeltaBW x (gammaFFM-gammaFM) x C² /(1-beta) x [1+(gammaFM +deltainit +Deltadelta)/(gammaFFM-gammaFM)]-C x (gammaFFM-gammaFM)/(1-beta) x LambertW{C x FMinit x Exp[C x (1 +FMinit) +DeltaBW]}

The corrected Equation 14 is:

DeltaBW=[(1-beta) x DeltaEI -Deltadelta x BWinit]/[gammaFFM +deltainit +Deltadelta-(gammaFFM -gammaFM) x Phi]

The corrected Equation 15 is:

d/dPhi{DeltaBW/DeltaEI} = (gammaFFM -gammaFM) x (1-beta)/[gammaFFM +deltainit-(gammaFFM -gammaFM) x Phi]² > 0

On 2013 Oct 28, Jamie Horder commented:

An independent group (Shumway S, 2012) confirmed the validity of the Calibrated Severity Score in 2012.

On 2014 Aug 31, Ivan Oransky commented:

This paper has been retracted: http://retractionwatch.com/2014/08/29/second-retraction-for-ob-gyn-in-ori-probe/ It's the second retraction for the last author: http://retractionwatch.com/2012/10/29/ori-investigating-university-of-florida-ob-gyn-researcher-accused-of-misconduct/

On 2016 Jan 23, Kenneth Witwer commented:

The link under "Availability" now opens a page in Japanese that does not appear to cover microarrays. Might this information be available elsewhere?

On 2016 Apr 12, Wolfgang Huber commented:

Dear Kenneth

Thank you for pointing this out. Apologies for the two months delay in response, I only saw your post today. The main online reference to the package remains the Bioconductor project: https://bioconductor.org/packages/arrayQualityMetrics . Running the code in the package vignette produces all the example reports mentioned in the above abstract. Moreover, we are also hosting a copy of these reports here: http://www-huber.embl.de/arrayQualityMetrics

The now defunct URL http://www.microarray-quality.org was associated with a grant-funded project (European Commission FP6) that achieved a lot of excellent outcomes but eventually reached the end of its life cycle (http://publications.jrc.ec.europa.eu/repository/handle/JRC60783).

On 2013 Nov 25, Dennis Eckmeier commented:

We did a follow-up electrophysiology study, which was published with some delay in 2013: Encoding of naturalistic optic flow by motion sensitive neurons of nucleus rotundus in the zebra finch (Taeniopygia guttata).

On 2013 Jun 15, Steven Salzberg commented:

This is a terrific article that rebuts the bad science promulgated by a popular children's doctor, who has been pushing an anti-vaccination message for years. Offit and Moser go through Sears' claims point-by-point and show how each one is misleading or simply wrong. Every pediatrician should read this.

On 2015 Jul 16, Mohamed Fahmy commented:

I wish to share in this valuable study, I think I had some exerince in this field

On 2015 May 07, Jeff Kiefer commented:

DAVID is continuously being used as evidenced by its numerous citations in current biomedical literature http://bit.ly/1FS1JgT. However, the data resources used by DAVID appear to not have been updated since 2009 http://david.abcc.ncifcrf.gov/helps/update.html. The fact that DAVID has not been updated going on 5 years calls into question the current utility of using this tool.

On 2016 May 26, Jeff Kiefer commented:

It looks like DAVID has been updated May 2016 https://david-d.ncifcrf.gov/content.jsp?file=release.html.

On 2017 Jul 11, Robin P Clarke commented:

"..why we are now seeing so many of these progressive diseases..."

One possibility could be that increased dietary vitamin C is causing increased iron absorbtion leading to progressive excess - even though obviously the vitamin C has benefits too (in the shorter term?).

On 2014 May 10, Ivan Oransky commented:

This paper has been retracted: http://retractionwatch.com/2014/05/09/more-retractions-for-researcher-who-says-he-will-no-longer-publish/

On 2017 Dec 06, Alexander Kraev commented:

The authors of this paper claim to have generated mouse strains that carry a PLN(R9C) transgene and 2, 1, or 0 endogenous PLN alleles. However, the mutant PLN resides on a transgenic array, with multiple copies the number of which the authors chose not to determine. It was independently found (https://doi.org/10.1101/075671), that the mutant PLN gene, assuming the transgene does not re-arrange during breeding, is present in 13 copies. This work also did not determine the actual mutant to wildtype PLN ratio at the mouse age, when the expression of the transgenic promoter is maximal (1 month), which would have estimated the scope of the overexpressed protein "bolus injection" that each strain received. Therefore, the conclusions of this paper should be interpreted with extreme caution, especially as such gene combinations never occur in human patients carrying PLN mutations.

On 2014 Apr 22, TS Shanmugarajan commented:

arun.laureate@gmail.com

On 2014 Nov 25, Harri Hemila commented:

The reasons for the retraction of this paper are described in: http://www.mv.helsinki.fi/home/hemila/PPH/CTA/CTA.html

On 2014 Jan 08, Tom Kindlon commented:

The inclusion of more quotes and qualitative information in the paper would have been useful

I think this is an interesting issue. However I think the paper would have benefited from quotes from what the participants said and generally more qualitative information. If healthcare providers are to improve on the situation, they need as much information as they can get about what exactly are the barriers.

In particular, I feel more qualitative information on "Knowledge, Attitudes, and Beliefs (KABs)" and "Healthcare System" would have been useful. Perhaps the data could be used to write another paper.

By the way, I find the use of the phrase (and acronym) "while those with insufficient fatigue (ISF)" in the abstract to be far from satisfactory. It seems to put fatigue onto a pedestal as the primary part of the definition of CFS. People for example may be in the ISF category because they don't have four of the case defining symptoms (while at the same time having "sufficient fatigue" i.e. the fatigue satisfies the entry criteria for CFS).

On 2015 Aug 19, Bill Cayley commented:

A good example of when "Less" is "more": https://lessismoreebm.wordpress.com/2015/08/03/episiotomy-for-vaginal-birth/

On 2015 Apr 02, Harri Hemila commented:

Errors in the Cochrane review (2009) on vitamin C for asthma by Kaur B, 2009.

In 2009, I found that the Cochrane review on vitamin C for asthma by Kaur B, 2009 had severe errors in the extraction and analysis of data. I wrote a Feedback to the Cochrane review and it is part of the 2009 Cochrane review, DOI, but the Feedback is also available as a separate document. Then I found out that the errors originated in the first Cochrane review version by Kaur B, 2001, see Pubmed Commons. Those errors were repeated in the updates by Ram FS, 2004 and by Kaur B, 2009, until the review was withdrawn by Kaur B, 2013. Because of the severe errors, the Cochrane review “vitamin C for asthma” misled readers for over a decade. This below is the Feedback (2009).

The Feedback (2009) on the Cochrane review by Kaur B, 2009

Schachter EN, 1982 carried out a trial with participants who had exercise-induced bronchoconstriction (EIB) so that each of the 12 participants was administered placebo and vitamin C at different times. Thus, each participant served as his or her own control (crossover). In Table III Schachter reported pre-post-exercise change of FEV1, so that the later FEV1 was measured 5 minutes after the exercise. Because two observations are measured from the same participant, the placebo period and vitamin C period difference in FEV1 change should be analysed using the paired t-test. The FEV1 data in Schachter’s Table III gives the mean difference between the vitamin C and placebo periods as 0.20 (SD 0.33) litres/s. Schachter EN, 1982 calculated t=2.13 in their paper, corresponding to P[1-tail]=0.028.

The Cochrane review by Kaur B, 2009 presents Schachter’s FEV1 changes in Analysis 1.2. However, data in Analysis 1.2 were extracted from Schachter’s Table II, which presents post-exercise FEV1 value measured immediately after the exercise. In EIB the fall in FEV1 occurs 5 to 20 minutes after the end of exercise, Rundell KW, 2008. Even Schachter EN, 1982 reported that, on the screening day, there was no fall in FEV1 immediately after exercise, but a significant fall 5 minutes after the exercise (Schachter Fig. 2). Therefore, extracting the FEV1 changes from Schachter’s Table II (FEV1 immediately after the exercise) is not reasonable if the purpose is to examine the effect of vitamin C on EIB.

Cohen HA, 1997 carried out an EIB trial with 20 participants who were administered placebo and vitamin C at different times (crossover). Post-exercise FEV1 was measured 8 minutes after the end of the exercise. The observations are paired also in this case and the results should be analysed using a paired test. 9 participants had FEV1 decrease >15% on both vitamin C and placebo treatments. 11 participants had >15% FEV1 decrease on placebo but <15% FEV1 decrease on vitamin C (Cohen 1997 Fig. 2). None of the participants had the opposite effect: <15% FEV1 decrease on placebo and >15% FEV1 decrease on vitamin C. In the paired 2x2 table analysis, the question is whether the difference between the corners (here 11 and 0) is statistically significant. This difference gives z=(11-0)/sqrt(11+0)=3.31, corresponding to P[1-tail]=0.0005.

A basic principle in controlled trial analysis requires that all randomized participants should be included in the analysis, the ITT principle. However, the Cochrane review by Kaur B, 2009 does not give the results for all 20 participants of (Cohen Fig. 2); Analysis 1.2 gives the results for only the 11 participants who had benefit of vitamin C (Cohen Table 2).

Furthermore, the Cochrane review by Kaur B, 2009 presents the average of post-exercise FEV1 values and not the pre-post-exercise difference in FEV1 in analysis 1.2. The post-exercise averages for Cohen’s Table 2 are 1.66 (SD 0.80) litres/s in the placebo period and 1.93 (SD 0.78) litres/s in the vitamin C period, P=0.42. However, given that the EIB is defined by the pre-post change in FEV1, the measurement of the effect on EIB should be based on the pre-post-exercise difference in FEV1, Rundell KW, 2008.

Furthermore, the relative effect calculated by Cohen HA, 1997 (Table 2; in %-units) is a better measure than the absolute value (in litres/s) because the relative effect adjusts for the great variation in baseline FEV1; the relative decrease in FEV1 is also used in guidelines, Rundell KW, 2008. Cohen HA, 1997 reports that the average relative fall in FEV1 is 25% in the placebo period and 5% in the vitamin C period (Table 2). Because the observations are paired, the paired t-test should be used. The average of the differences is 20% (SD 12%, SE 3.7%), which gives t=5.57, corresponding to P[1-tail]=0.00012. Thus, although the Cochrane review by Kaur B, 2009 presented only the 11 participants in which vitamin C was beneficial, their calculation suggests that even in this subgroup vitamin C was without effect, P=0.42, whereas a correct calculation gives a much smaller P-value.

In their EIB trial, Tecklenburg SL, 2007 studied 8 participants who were administered vitamin C and placebo at different times. They measured post-exercise FEV1 at 1, 5, 10, 15, 20, and 30 min after the exercise. Tecklenburg 2007 reported that the decrease in FEV1 in the vitamin C period was 6.4% (SE 2.4%) and decrease in the placebo period was 12.9% (SE 2.4%). Tecklenburg did not publish the paired comparison, nor original data so that the paired t-test could be calculated. Nevertheless, these averages give unpaired t=1.91, corresponding to P[1-tail]=0.038, which is conservative, the paired test P-value would be smaller.

Thus, three trials included in the Cochrane review by Kaur B, 2009 found benefit of vitamin C supplementation against EIB at 5 and 8 minutes after the exercise, Cohen HA, 1997, Schachter EN, 1982, or at the time of maximum fall in FEV1, Tecklenburg SL, 2007. The three P-values calculated above (0.028, 0.0005, 0.038) can be combined by using the Fisher's method. The combined P = 0.00007 provides evidence that the effects of vitamin C on EIB in these three trials are not explained by random fluctuations.

Analyses 1.1, 1.3 and 1.5 by Kaur B, 2009 present baseline data of two EIB trials discussed above (Cohen 1997; Schachter 1982). However, when a trial specifically examines the effect of vitamin C on EIB, the relevant outcome is the difference between the baseline and the 5-10 minutes post-exercise FEV1 values (the pre-post change), and not the baseline FEV1 value alone.

Finally, diagnosis of EIB by the change in FEV1 is well established Rundell KW, 2008. Therefore Kaur B, 2009 should have considered whether there is any benefit for readers from making additional analyses of the FVC and PEF values of the oldest trial by Schachter 1982. The more recent trials by Cohen 1997 and Tecklenburg 2007 did not report changes in FVC and PEF.

Postscript in 2015

Three years after my Feedback (above), in 2012, the Cochrane review authors replied to my criticism. Since their response had further errors, I wrote a second Feedback in 2013. The second Feedback was rejected by the Cochrane Airways group. Their rejection was inconsistent with the Principles of the Cochrane Collaboration (accessed April 2015). Eight principle is about “Ensuring quality: by applying advances in methodology, developing systems for quality improvement, and being open and responsive to criticism” [bold added]. The second Feedback (2013) is available as a separate document.

The above Feedback (2009) was extended to full length papers by Hemilä H, 2013 and Hemilä H, 2014.

On 2014 Apr 16, Tom Kindlon commented:

Prevalence estimates have been inflated due to the female predominance in the samples

(I previously posted this as a comment on the Br J Psych http://bjp.rcpsych.org/content/194/2/117.abstract/reply#content-block but not everyone may see it there)

We are not given information on the gender breakdown of Chronic Fatigue Syndrome (CFS) in this study but if it is in-line with other studies in the field, females would be much more likely to have CFS. Given a much larger percentage of the cohorts are female compared to the general population, this would mean that the (adjusted) prevalence rates for each country would be lower.

The total number of CFS cases found in a particular country cohort = Number of women with CFS + Number of men with CFS = P(CFS|F)N(F)+P(CFS|M)N(M) where F=Female, M=Male, N(F)=Number of Females, P(CFS|F)=Probability of a female having CFS, etc.

If one takes P(CFS|M)=0.25*P(CFS|F), which would be comparable to an approximate average of previous studies (for example, [1-4]), and assumes the number of men and women are equal in the 18-45 age bracket, the prevalence rates for CFS in the UK and Brazil are 1.65% and 1.21% respectively.

These figures in themselves are an upper bound on the true prevalence rates, given individuals in neither group went through rigorous and thorough individual assessments to exclude other conditions.

References:

[1] Bazelmans E, Vercoulen JH, Swanink CM, Fennis JF, Galama JM, van Weel C, van der Meer JW, Bleijenberg G. Chronic Fatigue Syndrome and Primary Fibromyalgia Syndrome as recognized by GPs. Fam Pract. 1999 Dec;16(6):602-4.

[2] Jason LA, Richman JA, Rademaker AW, Jordan KM, Plioplys AV, Taylor RR, McCready W, Huang CF, Plioplys S. A community-based study of chronic fatigue syndrome. Arch Intern Med. 1999 Oct 11;159(18):2129-37.

[3] Kim CH, Shin HC, Won CW. Prevalence of chronic fatigue and chronic fatigue syndrome in Korea: community-based primary care study. J Korean Med Sci. 2005 Aug;20(4):529-34.

[4] Reyes M, Nisenbaum R, Hoaglin DC, Unger ER, Emmons C, Randall B, Stewart JA, Abbey S, Jones JF, Gantz N, Minden S, Reeves WC. Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas. Arch Intern Med. 2003 Jul 14;163(13):1530-6.

On 2016 Nov 25, Egon Willighagen commented:

Many of the conversions of nicotine outlined in Figure 3 are available as machine readable data in WP1600 in WikiPathways: http://www.wikipathways.org/index.php/Pathway:WP1600

On 2015 Sep 11, Bill Cayley commented:

A good example of when "Less" is "more" in evaluating foot and ankle injuries: https://lessismoreebm.wordpress.com/?s=ankle

On 2014 Oct 23, Khalid Hassan commented:

One of my students did a similar study for her dissertation (currently in press). She found that the consumption of alchohol is the single biggest cause for car crashes in Turkey (35% of all crashes were associated with alcohol use). This finding is no suprise by itself and complements numerous previous studies. However, she did find a very interesting new correlation. Since her group uses anonymized data from insurance companies, they were able to test several hypothesis which were in coordance with car crashes caused by the consumption of alcohol. For example, she found that certain car brands are associated with a 75% increase in alcohol related car crashes. While this is most likely just correlational and not caused because these cars are harder to drive when under influence, she did find another correlation which might be very intruiging to study further.

Because most of her data was provided by insurance companies, they were very detailed and trustable. For example, they included police reports were applicable. Because of this data they were able to establish wether the alcoholic user was the perpetrator of these crashes or wether (while driving under influence is technically illegal) were the culprit of these crashes. In the vast majority of cases the drivers under the influence of alcohol were indeed the perpetrator (85%). However she did a very intruiging finding when digging deeper in the data that was provided by insurance companies. The use of dashcams Wikipedia has gained a lot of popularity in Turkey in recent years. This is caused by multiple factors, one of which has to do with reduced insurance costs by car insurance companies. When my student corrected for the use of a dashcam, she found dat the pepetrator/culprit(85%/15%) ratio decreased significantly to 55/45%. Of course this is a very intruiging finding which can be caused be several factors. This is the first research to examine the use of a dashcam in relation to the amount of alcohol related car crashes. We're currently looking to see wether these results hold up in a bigger sample size. While this is the first study to find this correlation, there is a previous Dutch one (Website) which found that the use of a dashcam is associated with a reduction of the involvement in general car crashes of 34%. Incidentally, our study showed the same result in alcohol related crashes, but further study is now urgently needed.

On 2015 Jun 02, thomas samaras commented:

Additional information on height, body size and longevity is available from:

Samaras TT. Evidence from eight different types of studies showing that smaller body size is related to greater longevity. Journal of Scientific Research & Reports. 2014: 3 (16): 2150-2160, 2014; article no. JSRR.2014.16.003.

Samaras TT. Human Scaling and Body Mass Index. In: Samaras TT (ed): Human Body Size and the Laws of Scaling: Physiological Performance, Growth, Longevity and Ecological Ramifications. New York: Nova Science Pub; 2007: pp 17-32.

He Q, Morris BJ, Grove JS, Petrovitch H, Ross W, Masaki KH, et al. Shorter men live longer: Association of height with longevity and FOXO3 genotype in American men of Japanese ancestry. Plos ONE 9(5): e94385. doi:10.1371/journal.pone.0094385.

Salaris L, Poulain M, Samaras TT. Height and survival at older ages among men born in an inland village in Sardinia (Italy), 1866-2006. Biodemography and Social Biology, 58:1, 1-13.

Bartke A. Healthy Aging: Is Smaller better? A mini-review. Gerontology 2012; 58:337-43.

On 2014 Sep 12, Ivan Oransky commented:

This study reported that vials cost 71% more in June 2008 than they did in January 2007. That increase has continued unabated, as we report at MedPage Today: http://www.medpagetoday.com/Endocrinology/Diabetes/47603