Bibliographic record
Abstract
EDITOR—Several correspondents have criticised the HOPE investigators for focusing on relative effects when the absolute benefit was very small. Twisselmann, in her summary of many responses on the website, comments: “It was noted that only the relative risk reduction was given in the study. This should have been accompanied by data on absolute risk reduction and number needed to treat and even number needed to harm (as per CONSORT guidelines).”1 This sentence may …
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Letter to the editor arguing about how CONSORT reporting guidance is applied; commentary about research reporting practice, not a study of it.
This letter comments on CONSORT reporting guidance and research reporting practice.
Letter discussing CONSORT trial-reporting guidance, commentary on research reporting rather than empirical metaresearch.
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 | 0.273 | 0.548 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.070 | 0.079 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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".