Open season: positive changes for increased transparency in the biomedical literature
Bibliographic record
Abstract
the Journal) follows a predictable series of steps: a research question is posed, a hypothesis is stated, a protocol is written (and registered) that dictates how the research will be conducted, the study is conducted, data are collected and analyzed, and then, finally, after the peer review process, a manuscript is published that describes the preceding steps.This traditional model of biomedical publication allows no access to a study's full protocol, the raw data being analyzed, or the detailed analysis plan (i.e., its statistical coding).These elements have all remained private information-known only to the researchers and exposed to little external scrutiny or verification.This lack of transparency has facilitated selective trial reporting (also known as publication bias), 1,2 selective outcome reporting, 3 mistakes in statistical analyses, 4 and the conduct of suspected 5 or known 6 fraudulent studies.These factors represent threats to the goal of basing our clinical care on the highest quality evidence.7 Significant changes have been promulgated over the past ten years to improve the transparency of research conduct and reporting.Invariably, these changes involve uncloaking hitherto concealed areas of clinical research.It is hoped the changes will permit increased external scrutiny, allow independent verification of results, reduce fraudulent research, and, ultimately, improve clinical outcomes.This editorial summarizes these changes for the readers of the Journal.
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 | 0.121 | 0.346 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.049 | 0.066 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".