Concordance between physiotherapists and physicians for care of patients with musculoskeletal disorders presenting to the emergency department
Bibliographic record
Abstract
BACKGROUND: Overcrowding in emergency departments (ED) is a major concern worldwide. To answer increasing health care demands, new models of care including advanced practice physiotherapists (APP) have been implemented in EDs. The purpose of this study was to assess diagnostic, treatment and discharge plan concordance between APPs and ED physicians for patients consulting to the ED for minor musculoskeletal disorders (MSKD). METHODS: Patients presenting to two EDs in Montréal (Canada) with a minor MSKD were recruited and independently assessed by an APP and ED physician. Both providers had to formulate diagnosis, treatment and discharge plans. Cohen's kappa (κ) and Prevalence and Bias Adjusted Kappas (PABAK) with associated 95%CI were calculated. Chi Square and t-tests were used to compare treatment, discharge plan modalities and patient satisfaction between providers. RESULTS: One hundred and thirteen participants were recruited, mean age was 50.3 ± 17.4 years old and 51.3% had an atraumatic MSKD. Diagnostic inter-rater agreement between providers was very good (κ = 0.81; 95% CI: 0.72-0.90). In terms of treatment plan, APPs referred significantly more participants to physiotherapy care than ED physicians (κ = 0.27; PABAK = 0.27; 95% CI: 0.07-0.45; p = 0.003). There was a moderate inter-rater agreement (κ = 0.46; PABAK = 0.64; 95% CI: 0.46-0.77) for discharge plans. High patient satisfaction was reported with no significant differences between providers (p = 0.57). CONCLUSION: There was significant agreement between APPs and ED physicians in terms of diagnosis and discharge plans, but more discrepancies regarding treatment plans. These results tend to support the integration of APPs in ED settings, but further prospective evaluation of the efficiency of these types of models is warranted.
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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 | 0.000 | 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) | 0.001 | 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".