Review article: Reporting Guidelines in the biomedical literature
Bibliographic record
Abstract
PURPOSE: Complete and accurate reporting of original research in the biomedical literature is essential for healthcare professionals to translate research outcomes appropriately into clinical practice. Use of reporting guidelines has become commonplace among journals, peer reviewers, and authors. This narrative review aims 1) to inform investigators, peer reviewers, and authors of original research in anesthesia on reporting guidelines for frequently reported study designs; 2) to describe the evidence supporting the use of reporting guidelines and checklists; and 3) to discuss the implications of widespread adoption of reporting guidelines by biomedical journals and peer reviewers. PRINCIPAL FINDINGS: Inadequate reporting can influence the interpretation, translation, and application of published research. As a result, reporting guidelines have been developed in order to improve the quality, completeness, and accuracy of original research reports. Biomedical journals increasingly endorse the use of reporting guidelines for authors and peer reviewers. To date, there is encouraging evidence that reporting guidelines improve the quality of reporting of published research, but the rates of both adoption of reporting guidelines and improvement in reporting are far from ideal. CONCLUSIONS: Use of reporting guidelines improves the quality of published research in biomedical journals. Nevertheless, the quality of research in the biomedical literature remains suboptimal despite increased adherence to reporting guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.378 | 0.743 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".