Design of a randomized trial of a multidisciplinary intervention for knee osteoarthritis: Pharmacist Initiated Intervention Trial in Osteoarthritis (PhIT-OA)
Bibliographic record
Abstract
3 3 Arthritis is the leading cause of disability in North America, with osteoarthritis (OA) the most prevalent disease within this classification. In Canada, the economic impact of musculoskeletal disease is second only to cardiovascular disease. It is estimated that, in the next 10 to 20 years, the prevalence of OA will increase by 50%, resulting in a large personal and societal burden. Knee OA, in particular, is common and disabling. Evidence-based management of knee OA involves the use of both nonpharmacological and pharmacological approaches. Recent studies, however, have shown gaps in identifying knee OA and in delivering the appropriate interventions. In the Pharmacist Identification of New, Diagnostically confirmed OA (PhIND-OA) study, we demonstrated that pharmacists could identify people with previously undiagnosed knee OA. A recent randomized controlled trial by Hay and colleagues indicated that enhanced pharmacist medication review was as effective as exercise in the short-term management of knee pain, and both were more effective than usual care. A strategy, therefore, that uses pharmacists to identify those individuals in the community with knee OA in order to perform a medication review and to provide a referral to other health care practitioners (i.e., primary care physicians and physiotherapists) may prove effective in addressing the care gap for knee OA. We hypothesize that R E S E A R C H I N P R O G R E S S
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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.
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".