Diagnostic Concordance Between Physiotherapist and Emergency Physicians for Patients With a Musculoskeletal Disorder in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Many patients present to the emergency department (ED) with a musculoskeletal disorder. In consequence, some hospitals have integrated autonomous management of musculoskeletal disorders by physiotherapists after triage. Although potential benefits were demonstrated, few studies have examined the agreement between physiotherapists' and emergency physicians' diagnosis. A better understanding of diagnostic concordance between physiotherapists and emergency physicians could inform the implementation of this care model. METHODS AND FINDINGS: Secondary analysis of data obtained through a pilot pragmatic randomised clinical trial. Data from patients presenting a minor musculoskeletal disorder managed by a physiotherapist and an emergency physician were used. Diagnostic concordance was examined using raw agreement and Gwet's first-order agreement coefficient. Thirty-six participants were assessed by both professionals (36.8 ± 18.2 years; W: 55.6%). Overall raw agreement was 86.1% and the diagnostic concordance was almost perfect (Gwet's AC1: 0.84, 95% CI: 0.69-0.98). The most common disagreement was when physiotherapists suspected a bone fracture or contusion, whereas emergency physicians diagnosed a ligament or meniscus disorder. CONCLUSIONS: Our results show excellent diagnostic concordance between physiotherapists and emergency physicians, thus supporting the safety of physiotherapy care in the ED. More studies are needed to confirm these results with a larger variety of diagnoses and age strata.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".