Do Quebec Physiotherapists Follow Evidence-Based Guidelines for Treating Knee Osteoarthritis?
Bibliographic record
Abstract
Purpose: The aim of this study was to document the types of treatment that Quebec physiotherapists use for knee osteoarthritis (OA), to assess whether treatment choices are consistent with current practice guidelines, and to explore the factors associated with using certain treatments. Method: Physiotherapists in Quebec answered a cross-sectional survey that was sent through licensing bodies and social media contacts. The respondents were asked which treatments they would use for a typical case of knee OA. We used descriptive statistics to determine the proportions of physiotherapists who chose each treatment and explored the associations between the use of treatments and demographic factors. Results: A total of 195 Quebec physiotherapists participated in this study, 74.9% of whom were women. The most common treatment choices were mobility exercises (96.4%), followed by education (93.8%), strengthening exercises (85.1%), thermotherapy and cryotherapy (72.3%), manual therapy (53.3%), and electrotherapy (42.1%). Selecting either manual or electrotherapy was significantly associated with working in private practice. Conclusions: The great majority of physiotherapists included mobility and strengthening exercises, both of which are the recommended approaches for treating knee OA. Physiotherapists in private practice were more likely to select manual therapy and electrotherapy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".