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Record W2002042355 · doi:10.1080/0142159042000218669

An integrated approach to improving appropriate use of anti-inflammatory medication in the treatment of osteoarthritis in Québec (Canada): the CURATA model

2004· article· en· W2002042355 on OpenAlexafffundabout
Martin Labelle, Michèle Beaulieu, Daniel Paquette, Carl Fournier, Louis Bessette, D. Choquette, Elham Rahme, Robert Thivierge

Bibliographic record

VenueMedical Teacher · 2004
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsCégep de RimouskiMcGill University Health CentreHôpital Notre-DameMerck Canada Inc. (Canada)Université de Montréal
FundersUniversité de Montréal
KeywordsConcordanceMedicineOsteoarthritisMedical prescriptionIntervention (counseling)Physical therapyTest (biology)Continuing medical educationFamily medicineAlternative medicineScale (ratio)Continuing educationMedical educationNursingInternal medicine

Abstract

fetched live from OpenAlex

CURATA is a multifaceted continuing medical education (CME) intervention, developed with input from 12 healthcare organizations to address the gap between current and recommended osteoarthritis (OA) treatment of general practitioners in Québec, Canada. Focusing on appropriate prescription of non-steroidal anti-inflammatory drugs, including cyclooxygenase-2 selective inhibitors (coxibs), the intervention comprised small-group, case-based workshops modelled after the Script Concordance test, and a decision tool reflecting current evidence-based clinical practice guidelines. A self-reported questionnaire measured knowledge of recommended OA treatment on an eight-point scale. Participants (n = 381) showed a mean 10.1% improvement in questionnaire score immediately following the workshop (15.2% improvement relative to mean pre-workshop score). Knowledge was maintained for three months post-workshop.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.759

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations7
Published2004
Admission routes3
Has abstractyes

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