Can a physiotherapy student assume the role of an advanced practice physiotherapist in Orthopaedic surgery triage? A prospective observational study
Bibliographic record
Abstract
BACKGROUND: Advanced practice physiotherapists (APP) have helped improve accessibility to orthopaedic outpatient care. Several studies have validated the APP practice model in orthopaedic care, demonstrating high agreement between APPs and orthopaedic surgeons (OS) regarding diagnosis and management. However, as APPs tend to be experienced senior physiotherapists, such a study involving physiotherapy students (PS) has not yet been explored. The objective of this study was to evaluate the agreement for orthopaedic diagnoses and surgical triage between a PS and OSs. METHODS: A prospective study involving a final year PS and seven OSs was conducted in a university hospital, after the PS had undergone a three-week intensive training. Eighty-six adult patients referred to OSs for knee osteoarthritis, hip osteoarthritis or shoulder problem were independently evaluated by the PS, and then re-evaluated by an OS. The diagnoses and surgical triage recommendations of both clinicians were analyzed for agreement using raw percent agreement and Cohen's kappa. Patient satisfaction with the outpatient clinic experience was noted using a modified version of the Visit-Specific Satisfaction Instrument. RESULTS: Our sample consisted of 86 patients (mean age = 63.4 years). Reasons for consultation included shoulder problems (36%), knee osteoarthritis (52%) and hip osteoarthritis (12%). The raw percent agreement for diagnosis was 95.3%. The agreement for surgical triage was high (κ = 0.86, 95% CI: 0.74-0.98) with a raw agreement of 94.2%. Patient satisfaction was high. CONCLUSIONS: The PS and OSs made similar diagnoses and triage recommendations suggesting that clinical experience alone is not a prerequisite for physiotherapists to help increase accessibility to orthopaedic care.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".