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Risk of bias assessment scores.

2021· article· en· W6885865006 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsRisk assessmentCohortScale (ratio)Cohort studySelection bias

Abstract

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OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.231
metaresearch head score (Gemma)0.618
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.618
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0120.047
Bibliometrics0.0220.015
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0050.007
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.1090.006

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 head0.891
GPT teacher head0.572
Teacher spread0.319 · 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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
DomainMethods
GenreDataset

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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