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Record W1983818342 · doi:10.1503/cmaj.051625

Is there another way to take account of noncompliance in randomized controlled trials?

2006· article· en· W1983818342 on OpenAlexvenueno aff
Catherine Hewitt

Bibliographic record

VenueCanadian Medical Association Journal · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialIntervention (counseling)Alternative medicineComputer scienceResearch designMedicineMedical physicsPhysical therapyPsychiatrySurgeryPathologyStatistics

Abstract

fetched live from OpenAlex

Noncompliance is an important issue in the design and conduct of randomized controlled trials (RCTs). It arises when participants do not receive the treatment or intervention to which they were randomly allocated. For example, some participants invited to go through a screening program may not

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)0.847
metaresearch head score (Gemma)0.918
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.153
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8470.918
Meta-epidemiology (narrow)0.0080.008
Meta-epidemiology (broad)0.0330.026
Bibliometrics0.0130.014
Science and technology studies0.0070.030
Scholarly communication0.0200.044
Open science0.0140.011
Research integrity0.0510.052
Insufficient payload (model declined to judge)0.0060.002

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 head0.154
GPT teacher head0.386
Teacher spread0.232 · 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

Citations137
Published2006
Admission routes1
Has abstractyes

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