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Partially Adaptive Treatment Strategies

2023· other· en· W4389083440 on OpenAlex

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

VenueWiley StatsRef: Statistics Reference Online · 2023
Typeother
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcGill UniversityUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsAdaptive strategiesOutcome (game theory)Computer scienceOrder (exchange)Function (biology)Mathematical optimizationMathematicsBusiness

Abstract

fetched live from OpenAlex

Abstract Partially adaptive treatment strategies are rules for tailoring treatment decisions that are a function of a few selected characteristics of an individual. This type of treatment strategy can ignore the impact that the other characteristics of the individual may have on the treatment effect. In particular, an optimal partially adaptive treatment strategy provides treatment decision rules that only depend on the selected characteristics in order to optimize some outcome of interest. Such decision rules can notably be used for personalized medicine. This article provides an introduction to partially adaptive treatment strategies and methods for estimating an optimal partially adaptive treatment strategy.

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)0.001
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.259
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.629
GPT teacher head0.562
Teacher spread0.067 · 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