Bibliographic record
Abstract
There is clear guidance on the responsibilities of editors to ensure that the research they publish is of the highest possible quality. Poor reporting is unethical and directly impacts patient care. Reporting guidelines are a relatively recent development to help improve the accuracy, clarity, and transparency of biomedical publications. They have caught on, with hundreds of reporting guidelines now available. Some journals endorse reporting guidelines while a smaller number have used various approaches to implement them. Yet challenges remain - biomedical research is still not optimally reported despite the abundance of reporting guidelines. Electronic algorithms are now being developed to facilitate the choice of correct reporting guideline(s), while other tools are being integrated into journal editorial management processes. Universities need to consider whether it is responsible to advance careers of faculty based on poorly reported research which is of little societal value. If journals embraced auditing of the quality of articles they publish this would give them and their readers essential feedback from which to improve their product.
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 | 0.243 | 0.648 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.024 | 0.037 |
| Insufficient payload (model declined to judge) | 0.033 | 0.063 |
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".