Is Group-Based Physiotherapy a Cost-Effective Intervention Compared to Usual One-on-One Physiotherapy Care in the Management of Musculoskeletal Disorders in Active Military Personnel? An Economic Evaluation Alongside a Pragmatic Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: To conduct a cost-utility analysis of a group physiotherapy intervention, compared to usual care, for musculoskeletal disorders in Canadian military personnel. DESIGN: Economic evaluation alongside a pragmatic randomized clinical trial. METHODS: One hundred and twenty military members presenting with shoulder, knee, ankle, or low back pain were randomized to receive either usual one-on-one physiotherapy care or a group intervention. Cumulative health care costs were prospectively collected over 26 weeks from the perspective of the Canadian Armed Forces. The clinical outcome of the cost-utility analysis was the quality-adjusted life-year (QALY) estimated by the ED-5Q-5L (European Quality of Life 5 Dimensions 5 Level Version) at baseline, 6, 12, and 26 weeks. The incremental cost-effectiveness ratio (ICER) was estimated by the cost difference between interventions (in 2023 Canadian dollars [CAD$]) divided by the effect difference. RESULTS: The mean QALY gain was 0.011 in the group intervention, and 0.010 in the usual care. The average cost for a patient was CAD $532 in the group intervention and CAD $599 in the usual care. The ICER (–$67 000/QALY) indicated that the group intervention was cost-effective, as it costs less than usual care while providing comparable effectiveness. CONCLUSION: Group interventions were cost-effective compared to usual care for treating musculoskeletal disorders in military personnel. J Orthop Sports Phys Ther 2025;55(4):295-304. Epub 26 Feb 2025. doi: 10.2519/jospt.2025.12888
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".