Advanced practice physiotherapy care in emergency departments for patients with musculoskeletal disorders: a pragmatic cluster randomized controlled trial and cost analysis
Bibliographic record
Abstract
BACKGROUND: Advanced practice physiotherapy (APP) models of care where physiotherapists are primary contact emergency department (ED) providers are promising models of care to improve access, alleviate physicians' burden, and offer efficient centered patient care for patients with minor musculoskeletal disorders (MSKD). OBJECTIVES: To compare the effectiveness of an advanced practice physiotherapist (APPT)-led model of care with usual ED physician care for persons presenting with a minor MSKD, in terms of patient-related outcomes, health care resources utilization, and health care costs. METHODS: This trial is a multicenter stepped-wedge cluster randomized controlled trial (RCT) with a cost analysis. Six Canadian EDs (clusters) will be randomized to a treatment sequence where patients will either be managed by an ED APPT or receive usual ED physician care. Seven hundred forty-four adults with a minor MSKD will be recruited. The main outcome measure will be the Brief Pain Inventory Questionnaire. Secondary measures will include validated self-reported disability questionnaires, the EQ-5D-5L, and other health care utilization outcomes such as prescription of imaging tests and medication. Adverse events and re-visits to the ED for the same complaint will also be monitored. Health care costs will be measured from the perspective of the public health care system using time-driven activity-based costing. Outcomes will be collected at inclusion, at ED discharge, and at 4, 12, and 26 weeks following the initial ED visit. Per-protocol and intention-to-treat analyses will be performed using linear mixed models with a random effect for cluster and fixed effect for time. DISCUSSION: MSKD have a significant impact on health care systems. By providing innovative efficient pathways to access care, APP models of care could help relieve pressure in EDs while providing efficient care for adults with MSKD. TRIAL REGISTRATION: ClinicalTrials.gov NCT05545917 . Registered on September 19, 2022.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".