Feasibility of the SOAR (Stop OsteoARthritis) program
Bibliographic record
Abstract
Objective: Assess the feasibility of a virtually-delivered, physiotherapist-guided knee health program (SOAR) that targets self-management of knee health and osteoarthritis risk after an activity-related knee injury. Design: In this quasi-experimental feasibility study, individuals with varied lived experience of knee trauma completed a 4-week SOAR program. This included: 1) Knee Camp (group education, 1:1 exercise and activity goal-setting); 2) weekly home-based exercise and activity program with tracking, and; 3) weekly 1:1 physiotherapy-guided action-planning. SOAR program feasibility was assessed with implementation (attrition, adherence, intervention fidelity), practicality (adverse events, goal completion), acceptability and efficacy (change in Knee injury and Osteoarthritis Outcome Score subscales, Patient Specific Functional Scale (PSFS), Godin Leisure-Time Exercise Questionnaire (GLTEQ), Partner in Health Scale (PHS)) outcomes. Descriptive statistics, disaggregated by gender, were calculated. Results: Thirty participants (60% women, median (min-max) age 30 years (19-50), time from injury 5.6 years (1.2-25.2)) were enrolled. No participant attrition or adverse events were reported, and 90% of mandatory program components were completed. Participants rated their adherence at 80%, and 96% of exercise-therapy and 95% of activity goals were fully or partially achieved. Both women and men reported significant group mean (95%CI) improvements in GLTEQ scores (women: 22 METS (6,37), men: 31 METS (8,54)), while women alone reported improvements in PHS (-7 (-11,-3) and PSFS (1.7 (0.6,2.8) scores. Conclusion: The SOAR program is feasible for persons at various timepoints post-knee trauma, and gender may be an important consideration for SOAR implementation and assessment. A randomized controlled trial to assess intervention efficacy is warranted.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".