Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To determine whether the adoption of the CONSORT checklist is associated with improvement in the quality of reporting of randomised controlled trials (RCTs). DATA SOURCES: MEDLINE, EMBASE, Cochrane CENTRAL, and reference lists of included studies and of experts were searched to identify eligible studies published between 1996 and 2005. STUDY SELECTION: Studies were eligible if they (a) compared CONSORT-adopting and non-adopting journals after the publication of CONSORT, (b) compared CONSORT adopters before and after publication of CONSORT, or (c) a combination of (a) and (b). Outcomes examined included reports for any of the 22 items on the CONSORT checklist or overall trial quality. DATA SYNTHESIS: 1128 studies were retrieved, of which 248 were considered possibly relevant. Eight studies were included in the review. CONSORT adopters had significantly better reporting of the method of sequence generation (risk ratio [RR], 1.67; 95% CI, 1.19-2.33), allocation concealment (RR, 1.66; 95% CI, 1.37-2.00) and overall number of CONSORT items than non-adopters (standardised mean difference, 0.83; 95% CI, 0.46-1.19). CONSORT adoption had less effect on reporting of participant flow (RR, 1.14; 95% CI, 0.89-1.46) and blinding of participants (RR, 1.09; 95% CI, 0.84-1.43) or data analysts (RR, 5.44; 95% CI, 0.73-36.87). In studies examining CONSORT-adopting journals before and after the publication of CONSORT, description of the method of sequence generation (RR, 2.78; 95% CI, 1.78-4.33), participant flow (RR, 8.06; 95% CI, 4.10-15.83), and total CONSORT items (standardised mean difference, 3.67 items; 95% CI, 2.09-5.25) were improved after adoption of CONSORT by the journal. CONCLUSIONS: Journal adoption of CONSORT is associated with improved reporting of RCTs.
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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 | 0.578 | 0.807 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.028 | 0.024 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".