Improving the reporting of randomised trials: the CONSORT Statement and beyond
Bibliographic record
Abstract
An extensive and growing number of reviews of the published literature demonstrate that health research publications have frequent deficiencies. Of particular concern are poor reports of randomised trials, which make it difficult or impossible for readers to assess how the research was conducted, to evaluate the reliability of the findings, or to place them in the context of existing research evidence. As a result, published reports of trials often cannot be used by clinicians to inform patient care or to inform public health policy, and the data cannot be included in systematic reviews. Reporting guidelines are designed to identify the key information that researchers should include in a report of their research. We describe the history of reporting guidelines for randomised trials culminating in the CONSORT Statement in 1996. We detail the subsequent development and extension of CONSORT and consider related initiatives aimed at improving the reliability of the medical research literature.
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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 | 0.817 | 0.903 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.025 | 0.019 |
| Bibliometrics | 0.022 | 0.035 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.030 | 0.046 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".