Physiotherapy for the Management of Polymyalgia Rheumatica: Results From a UK Cross‐Sectional Survey
Bibliographic record
Abstract
INTRODUCTION: Polymyalgia rheumatica (PMR) international management guidelines advocate patient education and individualised exercises but lack evidence and physiotherapy practice for PMR is unknown. PMR is typically treated with glucocorticoids, but side effects are frequent and concerning to patients. This study investigated UK physiotherapy practice in PMR. METHOD: Physiotherapists recruited from UK rheumatology and physiotherapy professional networks and university alumni were invited to complete a postal or online questionnaire. Topics included experiences of managing PMR, perceived role and value of physiotherapy in PMR, assessment and management priorities and physiotherapists' education about PMR. Results were summarised using descriptive statistics. RESULTS: 4288 invitations to participate were sent, and 1072 (25%) responses were received. Physiotherapy referrals for PMR were infrequent; 5.8% of respondents had treated ≥ 10 patients in the previous year. 80% of respondents advocated a physiotherapy role for PMR. 38% reported receiving some pre-registration education about PMR within their qualifying physiotherapy programme. Establishing patients' knowledge and understanding of PMR, pain levels, and ability to undertake activities of daily living were physiotherapists' assessment priorities. 90% of respondents promoted self-management approaches, including pacing and activity modification. Prioritising upper limbs, 89% prescribed individualised graded exercises to improve movement, muscle strength and activities of daily living function. CONCLUSION: A positive role for physiotherapy was reported for some people with PMR. Exercise, education and advice to improve daily functioning may be useful adjuncts to glucocorticoids. The limited PMR education for UK physiotherapists warrants attention. Further research is needed to evaluate the effectiveness of physiotherapy approaches for PMR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".