Diagnostic validity and triage concordance of a physiotherapist compared to physicians’ diagnoses for common knee disorders
Bibliographic record
Abstract
BACKGROUND: Emergence of more autonomous roles for physiotherapists warrants more evidence regarding their diagnostic capabilities. Therefore, we aimed to evaluate diagnostic and surgical triage concordance between a physiotherapist and expert physicians and to assess the diagnostic validity of the physiotherapist's musculoskeletal examination (ME) without imaging. METHODS: This is a prospective diagnostic study where 179 consecutive participants consulting for any knee complaint were independently diagnosed and triaged by two evaluators: a physiotherapist and one expert physician (orthopaedic surgeons or sport medicine physicians). The physiotherapist completed only a ME, while the physicians also had access to imaging to make their diagnosis. Raw agreement proportions and Cohen's kappa (k) were calculated to assess inter-rater agreement. Sensitivity (Se) and specificity (Sp), as well as positive and negative likelihood ratios (LR+/-) were calculated to assess the validity of the ME compared to the physicians' composite diagnosis. RESULTS: Primary knee diagnoses included anterior cruciate ligament injury (n = 8), meniscal injury (n = 36), patellofemoral pain (n = 45) and osteoarthritis (n = 79). Diagnostic inter-rater agreement between the physiotherapist and physicians was high (k = 0.89; 95% CI:0.83-0.94). Inter-rater agreement for triage recommendations of surgical candidates was good (k = 0.73; 95% CI:0.60-0.86). Se and Sp of the physiotherapist's ME ranged from 82.0 to 100.0% and 96.0 to 100.0% respectively and LR+/- ranged from 23.2 to 30.5 and from 0.03 to 0.09 respectively. CONCLUSIONS: There was high diagnostic agreement and good triage concordance between the physiotherapist and physicians. The ME without imaging may be sufficient to diagnose or exclude common knee disorders for a large proportion of patients. Replication in a larger study will be required as well as further assessment of innovative multidisciplinary care trajectories to improve care of patients with common musculoskeletal disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".