Pharmacist‐initiated intervention trial in osteoarthritis: A multidisciplinary intervention for knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (OA) is a commonly undiagnosed condition and care is often not provided. Pharmacists are uniquely placed for launching a multidisciplinary intervention for knee OA. METHODS: We performed a cluster randomized controlled trial with pharmacies providing either intervention care or usual care (14 and 18 pharmacies, respectively). The intervention included a validated knee OA screening questionnaire, education, pain medication management, physiotherapy-guided exercise, and communication with the primary care physician. Usual care consisted of an educational pamphlet. The primary outcome was the pass rate on the Arthritis Foundation's quality indicators for OA. Secondary outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Lower Extremity Function Scale (LEFS), the Paper Adaptive Test-5D (PAT-5D), and the Health Utilities Index Mark 3 (HUI3). RESULTS: One hundred thirty-nine patients were assigned to the control (n = 66) and intervention (n = 73) groups. There were no differences between the groups in baseline measures. The overall quality indicator pass rate was significantly higher in the intervention arm compared to the control arm (difference of 45.2%; 95% confidence interval 34.5, 55.9). Significant improvements were observed for the intervention care group as compared to the usual care group in the WOMAC global, pain, and function scores at 3 and 6 months (all P < 0.01); the PAT-5D daily activity scores at 3 and 6 months (both P < 0.05); the PAT-5D pain scores at 6 months (P = 0.05); the HUI3 single-attribute pain scores at 3 and 6 months (all P < 0.05); and the LEFS scores at 6 months (P < 0.05). CONCLUSION: Pharmacists can launch a multidisciplinary intervention to identify knee OA cases, improve the utilization of treatments, and improve function, pain, and quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".