Subgroup effects should be examined using both relative and absolute effect measures
Bibliographic record
Abstract
Treatment effects can be measured on the relative scale (eg, risk ratios, odds ratios) or the absolute scale (eg, risk differences). If the baseline risk of an outcome is different between subgroups, the effect of the treatment will differ between subgroups on at least one scale (relative, absolute, or both). We illustrate this using two examples from the literature where only relative effects were estimated, but conclusions about subgroup differences would likely have changed had absolute effects also been considered. To identify all meaningful subgroup differences, researchers and clinicians should compare effects on the relative and absolute scale.
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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 | 0.467 | 0.771 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".