Responsible reporting of health research studies: transparent, complete, accurate and timely
Bibliographic record
Abstract
Complete, accurate and transparent reporting is an integral part of responsible research conduct. However, many studies have shown that health research publications frequently lack crucial information. Reporting guidelines like the CONSORT Statement help to improve the quality of research reports. Unfortunately, their uptake by journals and authors is still limited and does not maximize their potential. The EQUATOR Network, a new international initiative, leads the effort to promote transparent reporting of research and the use of reporting guidelines. It provides online resources and training relating to the reporting of health research, and assists in the development, dissemination and implementation of reporting guidelines (www.equator-network.org). Poor reporting practices can be decreased only through close collaboration of all parties involved in research and its publication; EQUATOR can facilitate the process.
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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 | 0.663 | 0.802 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".