How Do Physical Therapists Treat People with Knee Osteoarthritis, and What Drives Their Clinical Decisions? A Population-Based Cross-Sectional Survey
Bibliographic record
Abstract
Purpose: It is unclear how physical therapists in Florida currently treat people with knee osteoarthritis and whether current best evidence is used in clinical decision making. Methods: We conducted a survey of physical therapists in Florida. We assessed the perceived effectiveness and actual use of physical therapy (PT) interventions and quantified the association between the actual use of interventions and different characteristics of physical therapists. Results: A total of 413 physical therapists completed the survey. Most respondents perceived therapeutic exercise (94%) and education (93%) as being effective or very effective. Interventions least perceived as effective or very effective were electrotherapy (28%), wedged insole (20%), and ultrasound (19%). Physical therapists who followed the principles of evidence-based practice were more likely to use therapeutic exercise (OR 3.89; 95% CI: 1.21, 12.54) and education (OR 3.63; 95% CI: 1.40, 9.43) and less likely to use ultrasound (OR 0.32; 95% CI: 0.16, 0.63) and electrotherapy (OR 0.32; 95% CI: 0.17, 0.58). Results also indicated that older physical therapists were more likely to use ultrasound (OR 3.57; 95% CI: 1.60, 7.96), electrotherapy (OR 2.53; 95% CI: 1.17, 5.47), kinesiology tape (OR 3.82; 95% CI: 1.59, 9.18), and ice (OR 1.95; 95% CI: 1.02, 3.73). Conclusions: In line with clinical guidelines, most physical therapists use therapeutic exercise and education to treat people with knee osteoarthritis. However, interventions that lack scientific support, such as electrotherapy and ultrasound, are still used. A modifiable therapist characteristic, adherence to evidence-based practice, is positively associated with the use of interventions supported by scientific evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".