On 2014 Jul 31, Paul Glasziou commented:

We agree with Bastian's comments, but getting authors to do this will not occur soon! However, journals could potentially use automated tools to assist with extraction of trial details and provision of UTNs. Manual extraction, whether by author, journal, or reviewer, should become a thing of the past.

On 2014 Jul 26, Hilda Bastian commented:

A thorough and valuable breakdown of what could and should be automated in systematic reviewing. One additional important strategy lies in the hands of everyone doing (and publishing) clinical trials and systematic reviews: following the IJCME recommendation to include the clinical trial registry identification number of every trial at the end of abstracts. This needs to be done using the specific, unaltered formats for each registry in which a study is included, so that IDs are easily retrievable - and the IDs should be with every cited study inside the systematic review, too. Using the WHO's Universal Trial Number (UTN) would also help with the critical, and time-consuming, task of study de-duplication.

The issue raised in this article of some databases of manually extracted trial data not being publicly available is an important one. It's worth noting, though, that this is not because it's not possible: systematic reviewers have the option of using the open and collaborative public infrastructure of the SRDR (Systematic Review Data Repository) (Ip S, 2012).

Another option to add to the list of ways of improving the snowballing technique for identifying studies: using the related articles function in PubMed. That's been found to be useful in empirical studies of techniques for updating systematic reviews (Shojania KG, 2007).

(Disclosure: I work on projects related to systematic reviews at the NCBI (National Center for Biotechnology Information, U.S. National Library of Medicine), which is also responsible for ClinicalTrials.gov.)

On 2014 Jul 26, Hilda Bastian commented:

A thorough and valuable breakdown of what could and should be automated in systematic reviewing. One additional important strategy lies in the hands of everyone doing (and publishing) clinical trials and systematic reviews: following the IJCME recommendation to include the clinical trial registry identification number of every trial at the end of abstracts. This needs to be done using the specific, unaltered formats for each registry in which a study is included, so that IDs are easily retrievable - and the IDs should be with every cited study inside the systematic review, too. Using the WHO's Universal Trial Number (UTN) would also help with the critical, and time-consuming, task of study de-duplication.

The issue raised in this article of some databases of manually extracted trial data not being publicly available is an important one. It's worth noting, though, that this is not because it's not possible: systematic reviewers have the option of using the open and collaborative public infrastructure of the SRDR (Systematic Review Data Repository) (Ip S, 2012).

Another option to add to the list of ways of improving the snowballing technique for identifying studies: using the related articles function in PubMed. That's been found to be useful in empirical studies of techniques for updating systematic reviews (Shojania KG, 2007).

(Disclosure: I work on projects related to systematic reviews at the NCBI (National Center for Biotechnology Information, U.S. National Library of Medicine), which is also responsible for ClinicalTrials.gov.)

On 2014 Jul 31, Paul Glasziou commented:

We agree with Bastian's comments, but getting authors to do this will not occur soon! However, journals could potentially use automated tools to assist with extraction of trial details and provision of UTNs. Manual extraction, whether by author, journal, or reviewer, should become a thing of the past.