Reporting transparency and completeness in Trials: Paper 2 - reporting of randomised trials using registries was often inadequate and hindered the interpretation of results
Bibliographic record
Abstract
OBJECTIVE: Registries are important data sources for randomized controlled trials (RCTs), but reporting of how they are used may be inadequate. The objective was to describe the current adequacy of reporting of RCTs using registries. STUDY DESIGN AND SETTING: We used a database of trials using registries from a scoping review supporting the development of the 2021 CONSORT extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE). Reporting completeness of 13 CONSORT-ROUTINE items was assessed. RESULTS: We assessed reports of 47 RCTs that used a registry, published between 2011 and 2018. Of the 13 CONSORT-ROUTINE items, 6 were adequately reported in at least half of reports (2 in at least 80%). The 7 other items were related to routinely collected data source eligibility (32% adequate), data linkage (8% adequate), validation and completeness of data used for outcome assessment (8% adequate), validation and completeness of data used for participant recruitment (0% adequate), participant flow (9% adequate), registry funding (6% adequate) and interpretation of results in consideration of registry use (25% adequate). CONCLUSION: Reporting of trials using registries was often poor, particularly details on data linkage and quality. Better reporting is needed for appropriate interpretation of the results of these trials.
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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 | 0.704 | 0.889 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".