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Record W3091924430 · doi:10.1002/sim.8757

Analysis of time-to-event for observational studies:Guidance to the use of intensity models

2021· article· en· W3091924430 on OpenAlexafffund
Maja Pohar Perme, Hans C. van Houwelingen, Richard J. Cook, Pierre Joly, Torben Martinussen, Jeremy M. G. Taylor, Michał Abrahamowicz, Terry M. Therneau

Bibliographic record

VenueDeep Blue (University of Michigan) · 2021
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsMcGill UniversityUniversity of Waterloo
FundersNational Cancer InstituteCanadian Institutes of Health ResearchJavna Agencija za Raziskovalno Dejavnost RSNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCensoring (clinical trials)Computer scienceCovariateProportional hazards modelHazardObservational studyEvent (particle physics)EconometricsRegression analysisGoodness of fitStatisticsData miningMachine learningMathematics

Abstract

fetched live from OpenAlex

Peer Reviewed

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.

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)0.190
metaresearch head score (Gemma)0.596
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.810
Threshold uncertainty score0.999

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1900.596
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0060.006
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0080.004
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0440.023

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 head0.316
GPT teacher head0.359
Teacher spread0.043 · 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

Citations70
Published2021
Admission routes2
Has abstractyes

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