Validation of an advanced practice physiotherapy model of care in an orthopaedic outpatient clinic
Bibliographic record
Abstract
BACKGROUND: In Canada, new models of orthopaedic care involving advanced practice physiotherapists (APP) are being implemented. In these new models, aimed at improving the efficiency of care for patients with musculoskeletal disorders, APPs diagnose, triage and conservatively treat patients. Formal validation of the efficiency and appropriateness of these emerging models is scarce. The purpose of this study is to assess the diagnostic agreement of an APP compared to orthopaedic surgeons as well as to assess treatment concordance, healthcare resource use, and patient satisfaction in this new model. METHODS: 120 patients presenting for an initial consult for hip or knee complaints in an outpatient orthopaedic hospital clinic in Montreal, Canada, were independently assessed by an APP and by one of three participating orthopaedic surgeons. Each health care provider independently diagnosed the patients and provided triage recommendations (conservative or surgical management). Proportion of raw agreement and Cohen's kappa were used to assess inter-rater agreement for diagnosis, triage, treatment recommendations and imaging tests ordered. Chi-Square tests were done in order to compare the type of conservative treatment recommendations made by the APP and the surgeons and Student t-tests to compare patient satisfaction between the two types of care. RESULTS: The majority of patients assessed were female (54%), mean age was 54.1 years and 91% consulted for a knee complaint. The raw agreement proportion for diagnosis was 88% and diagnostic inter-rater agreement was very high (κ=0.86; 95% CI: 0.80-0.93). The triage recommendations (conservative or surgical management) raw agreement proportion was found to be 88% and inter-rater agreement for triage recommendation was high (κ=0.77; 95% CI: 0.65-0.88). No differences were found between providers with respect to imaging tests ordered (p≥0.05). In terms of conservative treatment recommendations made, the APP gave significantly more education and prescribed more NSAIDs, joint injections, exercises and supervised physiotherapy (p<0.05). Patient satisfaction was significantly higher for APP care than for the surgeons care (p<0.05). CONCLUSION: The diagnoses and triage recommendations for patients with hip and knee disorders made by the APP were similar to the orthopaedic surgeons. These results provide evidence supporting the APP model for 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 | 0.051 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".