MétaCan
Menu
Back to cohort
Record W4322758210 · doi:10.1111/ajo.13656

Subgroup effects should be examined using both relative and absolute effect measures

2023· article· en· W4322758210 on OpenAlex
Peter Socha, Sam Harper, Jennifer A. Hutcheon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian and New Zealand Journal of Obstetrics and Gynaecology · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsRelative riskAbsolute risk reductionMedicineAbsolute (philosophy)Subgroup analysisScale (ratio)Odds ratioStatisticsDemographyInternal medicineConfidence intervalMathematics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.069
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.450
GPT teacher head0.479
Teacher spread0.029 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it