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Record W4412443540 · doi:10.1016/j.jval.2025.04.1292

MSR141 Reconciling Discrepancies in Results with Different Population-adjusted Indirect Comparison (PAIC) Methods

2025· article· en· W4412443540 on OpenAlexaff
K. Jack Ishak, Conor Chandler, Irina Proskorovsky, Kyle Fahrbach, Venediktos Kapetanakis

Bibliographic record

VenueValue in Health · 2025
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsStatisticsEconometricsMathematics

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)0.328
metaresearch head score (Gemma)0.696
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.672
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3280.696
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.009
Bibliometrics0.0040.006
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0060.006
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.676
GPT teacher head0.611
Teacher spread0.065 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractno

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