Factors related to physical therapy management in patients with knee osteoarthritis
Bibliographic record
Abstract
Background: Most physical therapists in developed countries use evidence-based physical therapy for patients with knee osteoarthritis (OA). Conversely, little is known concerning treatment used by Thai physical therapists (PTs) in such patients. Objective: To study the relationship among characteristics of PTs, patients with knee OA, and type of physical therapy treatments. Methods: This survey applied three sets of questionnaires to collect data. Questionnaire Set-A collected PTs’ information and experience in treating knee OA. Questionnaires Set-B collected patients’ information regarding history of knee OA, pain, disability, and function (using pain scale and the modified Thai version of the Western Ontario and McMaster Universities Arthritis Index (Thai-WOMAC). Questionnaire Set-C collected data on the treatment methods provided to each patient. Result: Seventy-six PTs working in the 40 secondary care hospitals across Eastern Thailand, and 267 patients with knee OA participated in this study. Exercise (93.63%) was the most common treatment used. Most PTs applied a combination of techniques to relieve pain and improve knee function. The exercise was associated with the total modified WOMAC score and the patient’s BMI (p<0.05). Conclusion: Most Thai PTs using exercise for treating knee OA that was consistent with the clinical guidelines. Some patient characteristics influenced the PTs’ selection of treatment including age, BMI, pain, stiffness, and functions of patients. Moreover, PTs’ increased skill.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".