Impact of a web-based tool (WebCONSORT) to improve the reporting of randomised trials: results of a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: The CONSORT Statement is an evidence-informed guideline for reporting randomised controlled trials. A number of extensions have been developed that specify additional information to report for more complex trials. The aim of this study was to evaluate the impact of using a simple web-based tool (WebCONSORT, which incorporates a number of different CONSORT extensions) on the completeness of reporting of randomised trials published in biomedical publications. METHODS: We conducted a parallel group randomised trial. Journals which endorsed the CONSORT Statement (i.e. referred to it in the Instruction to Authors) but do not actively implement it (i.e. require authors to submit a completed CONSORT checklist) were invited to participate. Authors of randomised trials were requested by the editor to use the web-based tool at the manuscript revision stage. Authors registering to use the tool were randomised (centralised computer generated) to WebCONSORT or control. In the WebCONSORT group, they had access to a tool allowing them to combine the different CONSORT extensions relevant to their trial and generate a customised checklist and flow diagram that they must submit to the editor. In the control group, authors had only access to a CONSORT flow diagram generator. Authors, journal editors, and outcome assessors were blinded to the allocation. The primary outcome was the proportion of CONSORT items (main and extensions) reported in each article post revision. RESULTS: A total of 46 journals actively recruited authors into the trial (25 March 2013 to 22 September 2015); 324 author manuscripts were randomised (WebCONSORT n = 166; control n = 158), of which 197 were reports of randomised trials (n = 94; n = 103). Over a third (39%; n = 127) of registered manuscripts were excluded from the analysis, mainly because the reported study was not a randomised trial. Of those included in the analysis, the most common CONSORT extensions selected were non-pharmacologic (n = 43; n = 50), pragmatic (n = 20; n = 16) and cluster (n = 10; n = 9). In a quarter of manuscripts, authors either wrongly selected an extension or failed to select the right extension when registering their manuscript on the WebCONSORT study site. Overall, there was no important difference in the overall mean score between WebCONSORT (mean score 0.51) and control (0.47) in the proportion of CONSORT and CONSORT extension items reported pertaining to a given study (mean difference, 0.04; 95% CI -0.02 to 0.10). CONCLUSIONS: This study failed to show a beneficial effect of a customised web-based CONSORT checklist to help authors prepare more complete trial reports. However, the exclusion of a large number of inappropriately registered manuscripts meant we had less precision than anticipated to detect a difference. Better education is needed, earlier in the publication process, for both authors and journal editorial staff on when and how to implement CONSORT and, in particular, CONSORT-related extensions. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01891448 [registered 24 May 2013].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.512 | 0.768 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".