Diagnostic concordance between physiotherapist and emergency physicians for patients with a musculoskeletal disorder in the emergency department
Bibliographic record
Abstract
ABSTRACT Question What is the level of diagnostic concordance between physicians and physiotherapists for patients consulting in an emergency department (ED) with musculoskeletal disorders (MSKDs)? Design Secondary analysis of data obtained through a pragmatic randomized controlled trial, physiotherapist (PT) and emergency physicians (EPs) unblinded, per-protocol analysis. Participants 78 participants aged 18 to 80 years presenting with a minor MSKD were recruited and data from the 40 participants randomized in the intervention group were used (mean age 36.6 yrs (SD: 17.3, IQR: 22.0; 46.8); women: 55%). Outcome measures Diagnostic concordance was established using the International Classification of Diseases 11 (ICD-11) and examined between EP and PT using raw agreement and Gwet’s first-order agreement coefficient (AC1). Results Forty participants were recruited and 36 were assessed by the PT and EP (36.8 ± 18.2 years old; 55.6% women). Overall raw agreement was 86.1% and an almost perfect diagnostic concordance was observed between PT and EP (Gwet’s AC1: 0.84, 95% CI: 0.69 – 0.98). The most common reason for disagreement was suspected bone fracture or contusion in comparison with ligament or meniscus disorder. Conclusion Results from this study indicate that overall diagnostic concordance between PT and EP in the ED for patients consulting with MSKDs is almost perfect. These results tend to support the safety of such models of care. However, more studies are needed to confirm these results as the EP wasn’t always independent and the classification used was imprecise. Registration # NCT04009369
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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 | 0.052 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".