Cost-Effectiveness of Physiotherapy Services for Chronic Condition Management: A Systematic Review of Economic Evaluations Conducted Alongside Randomized Controlled Trials
Bibliographic record
Abstract
Purpose: To determine the cost-effectiveness of physiotherapy (PT) to manage individuals with chronic conditions. Method: Design: Systematic review of randomized controlled trials (RCTs). Eligibility: RCTs with adult participants diagnosed with ≥1 chronic condition, an intervention delivered or supervised by a physiotherapist, including an economic evaluation of the intervention provided. Procedure: Eight databases were searched. Title/abstract screening, full-text review, and data extraction were performed in duplicate. The quality of included studies was assessed using Cochrane's Risk of Bias Assessment 2.0. Results: Fifty-three articles were included in this review. Fifteen compared PT to no PT; 38 compared novel PT to conventional PT. Of the studies comparing PT to no PT, 53% found PT to be cost-effective. Of the studies comparing novel to conventional PT, 55% found novel interventions were cost-effective. Overall, PT was cost-effective in most studies related to arthritis, chronic low back pain, and chronic neck pain. The heterogeneity of study characteristics limited the ability to perform a meta-analysis. Conclusions: Over half of included studies reported PT to be cost-effective. Future high quality RCTs performing rigorous economic evaluations are needed to determine the cost-effectiveness of different interventions delivered or supervised by a PT to prevent disability for those with chronic conditions.
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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 | 0.041 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".