Could Primary Contact Physical Therapy Improve Emergency Department Care While Being Efficient? A 3-Month Cost-Utility Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost utility of 2 emergency department (ED) care models: management by an emergency physician (EP; usual care), and management by a primary contact physical therapist (PT) and an EP (PT + EP; intervention). DESIGN: Cost-utility analysis based on data collected during a pilot pragmatic randomized clinical trial over a 3-month period (NCT04009369). METHODS: We measured health-related quality of life (HRQoL) and health resource use at baseline, and 1 and 3 months, using the EuroQol 5-Dimension 5-Level questionnaire (EQ-5D-5L) and a standardized health care resource use questionnaire. Responses to the EQ-5D-5L were transformed into utility scores (Canadian conversion algorithm), and then into quality-adjusted life years (QALYs) using area-under-the-curve analyses. Costs and QALYs were used to derive incremental cost-effectiveness ratios for each perspective. We conducted a complete case analysis (main analysis), and missing data were imputed using multiple imputation (sensitivity analysis). RESULTS: After 3 months, participants managed by the PT + EP had a QALY gain of 0.195 (95% confidence interval [CI]: 0.179, 0.209), compared to 0.182 (95% CI: 0.168, 0.195) for those managed by the EP alone. The average total cost in the PT + EP group for the public payer was $469.23/patient (95% CI: $269.30, $708.85) and $878.37/patient for society (95% CI: $559.72, $1208.23), compared with $804.70/patient (95% CI: $225.58, $1972.78) and $1288.76/patient (95% CI: $551.84, $2452.48), respectively, in the EP group (2019 CAD). PT + EP management was dominant for the public payer and Canadian society perspectives. CONCLUSION: The addition of PTs in EDs may reduce expenses for the public payer and society, while improving HRQoL. J Orthop Sports Phys Ther 2026;56(2):109-118. Epub 27 November 2025. doi:10.2519/jospt.2025.13429
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".