CURATA: A patient health management program for the treatment of osteoarthritis in Québec: an integrated approach to improving the appropriate utilization of anti-inflammatory/analgesic medications.
Bibliographic record
Abstract
OBJECTIVES: To identify gaps in current osteoarthritis (OA) care in Quebec, Canada, and to implement and evaluate interventions to promote appropriate use of evidence-based medicine. STUDY DESIGN: Pretest and posttest; analysis of the Quebec health insurance database. METHODS: CURATA is a patient health management program utilizing an evidence-based approach for OA treatment. Evaluation of the current level of care revealed major gaps in physicians' knowledge of (1) risk factors for gastrointestinal (GI) toxicity associated with nonsteroidal anti-inflammatory drugs (NSAIDs); (2) NSAID-induced toxicity associated with long-term administration and contraindications for NSAID use in patients with hypertension, cardiovascular disease, or renal insufficiency; (3) choice of cytoprotection; and (4) use of nonpharmacologic treatments for OA. The CURATA intervention consisted of educational workshops, with and without presentation of a decision tree regarding appropriate use of pharmacologic and nonpharmacologic OA treatments. Participating physicians were asked to complete an 8-item questionnaire before and after the workshop, as well as 3 and 6 months later, to test their immediate and remote knowledge of treatment choices. The prescribing patterns of GPs also were evaluated through analysis of the Quebec health insurance database. RESULTS: The participating physicians were better immediate and remote risk assessors of GI bleeding and made more appropriate treatment choices (15.2% improvement relative to mean preworkshop score). CONCLUSION: These evidence-based interventions were successful not only in improving the physicians' knowledge regarding the diagnosis and management of OA, but also--more importantly--in changing their behavior to make more appropriate therapy choices for their patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".