Bibliographic record
Abstract
A lack of rigour in animal studies is damaging the quality of research and can send clinical trials up blind alleys, wasting time and money, an international study has found.1 The findings were echoed in a second, unconnected study from McGill University in Montreal, Canada, which showed that animal trials of the kidney cancer drug sunitinib (Sutent) overestimated its effect by 45%.2 Both teams concluded that improvements are needed in how preclinical studies in animals are planned, organised, and published. Malcolm Macleod, of the University of Edinburgh, UK, who was lead author of the first study, told a briefing at the Science Media Centre in London that these methods were no secret, as they are already used in clinical trials in humans: randomisation, blinding, sample size calculations, and declarations of interest, for example. Macleod and colleagues1 used three …
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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 | 0.141 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.054 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".