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Adherence measurement and patient recruitment methods are poor in intervention trials to improve patient adherence

2014· review· en· W1998155248 on OpenAlexaff
Rebecca Jeffery, Tamara Navarro, Nancy L Wilczynski, Emma Iserman, Arun Keepanasseril, Bhairavi Sivaramalingam, Thomas Agoritsas, R. Brian Haynes

Bibliographic record

VenueJournal of Clinical Epidemiology · 2014
Typereview
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsHealth Sciences CentreMcMaster UniversityDalhousie University
Fundersnot available
KeywordsInterquartile rangeMedicineConfidence intervalPsychological interventionPhysical therapyIntervention (counseling)Randomized controlled trialSample size determinationClinical trialInternal medicineNursingStatistics

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)0.213
metaresearch head score (Gemma)0.367
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.787
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2130.367
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0150.005
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0040.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.836
GPT teacher head0.667
Teacher spread0.168 · 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 designSystematic review
DomainMethods
GenreReview

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

Citations49
Published2014
Admission routes1
Has abstractno

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