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The selection of comparators for randomized controlled trials of health-related behavioral interventions: recommendations of an NIH expert panel

2019· review· en· W2918272982 on OpenAlexaff
Kenneth E. Freedland, ­Abby C. King, Walter T. Ambrosius, Evan Mayo‐Wilson, David C. Mohr, Susan M. Czajkowski, Lehana Thabane, Linda M. Collins, George W. Rebok, Shaun Treweek, Thomas D. Cook, Jack D. Edinger, Catherine M. Stoney, Rebecca Campo, Deborah Lee Young-Hyman, William T. Riley

Bibliographic record

VenueJournal of Clinical Epidemiology · 2019
Typereview
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsMcMaster UniversityImpact
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsComparatorSelection (genetic algorithm)MedicinePsychological interventionRandomized controlled trialComputer sciencePsychiatryArtificial intelligence

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)0.423
metaresearch head score (Gemma)0.501
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score0.712

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4230.501
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0310.032
Bibliometrics0.0100.007
Science and technology studies0.0030.005
Scholarly communication0.0100.007
Open science0.0160.006
Research integrity0.0170.018
Insufficient payload (model declined to judge)0.0080.003

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 head0.929
GPT teacher head0.708
Teacher spread0.221 · 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 designNot applicable
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

Citations233
Published2019
Admission routes1
Has abstractno

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