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Record W3161848643 · doi:10.1016/j.jval.2021.02.011

Bias Assessment in Outcomes Research: The Role of Relative Versus Absolute Approaches

2021· article· en· W3161848643 on OpenAlexaff
Jennifer Stone, Usha Gurunathan, Edoardo Aromataris, Kathryn Glass, Peter Tugwell, Zachary Munn, Suhail A.R. Doi

Bibliographic record

VenueValue in Health · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsUniversity of Ottawa
FundersQatar National Research FundFonds National de la Recherche LuxembourgAustralian National UniversityQatar Foundation
KeywordsConsistency (knowledge bases)Independence (probability theory)StatisticsScale (ratio)Relative valueIntraclass correlationEconometricsQuality (philosophy)PsychologyMathematicsComputer scienceArtificial intelligencePsychometricsGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

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)0.669
metaresearch head score (Gemma)0.838
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.331
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6690.838
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0160.009
Bibliometrics0.0160.013
Science and technology studies0.0030.030
Scholarly communication0.0190.026
Open science0.0100.014
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0030.001

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 head0.821
GPT teacher head0.538
Teacher spread0.284 · 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

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations29
Published2021
Admission routes1
Has abstractno

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