Reducing bias and improving transparency in medical research: a critical overview of the problems, progress and suggested next steps
Bibliographic record
Abstract
In recent years there has been increasing awareness of problems that have undermined trust in medical research. This review outlines some of the most important issues including research culture, reporting biases, and statistical and methodological issues. It examines measures that have been instituted to address these problems and explores the success and limitations of these measures. The paper concludes by proposing three achievable actions which could be implemented to deliver significantly improved transparency and mitigation of bias. These measures are as follows: (1) mandatory registration of interests by those involved in research; (2) that journals support the 'registered reports' publication format; and (3) that comprehensive study documentation for all publicly funded research be made available on a World Health Organization research repository. We suggest that achieving such measures requires a broad-based campaign which mobilises public opinion. We invite readers to feedback on the proposed actions and to join us in calling for their implementation.
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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 | 0.247 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".