Economic evaluation of advanced practice physiotherapy models of care for upper extremity musculoskeletal disorders In Denmark: a registry-based cohort study
Bibliographic record
Abstract
BACKGROUND: Advanced practice physiotherapy (APP) models of care, which provide greater autonomy and responsibility to physiotherapists, have emerged as promising solutions to increase healthcare access while providing cost-effective care for MSK disorders. A formal health economic evaluation of these models has yet to be undertaken in Denmark. OBJECTIVES: To perform a registry-based economic evaluation of APP care versus standard care models for managing upper extremity MSK disorders in four Danish orthopedic clinics in the societal perspective. METHODS: Data related to sociodemographic, diagnoses, healthcare resource use, medication, costs, and sickness benefits within a two-year period after the initial consultation were extracted from Danish databases. Total healthcare costs including primary care (medical and rehabilitation), medication and hospital costs were calculated as well as productivity loss. Costs were converted to Euros 2022. Propensity score weighting was used to adjust for confounders. RESULTS: A total of 13,517 patients were included in the main analysis. Healthcare cost distribution differed between the two models with higher rehabilitation (mean difference [MD]: €18; 95% CI: 8 to 28) but lower medication (MD: -€50; 95% CI: -58 to -43) costs with the APP model of care. However, there was no significant difference in total healthcare costs (MD: €86; 95% CI: -305 to 476) nor in productivity loss (MD: €197; 95% CI: -1678 to 2072) between the two models. CONCLUSION: APP care results in similar total healthcare costs and productivity loss when compared to standard care for adults with upper extremity MSK disorders.
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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 | 0.018 | 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".