A Physiotherapy Triage Service for Orthopaedic Surgery: An Effective Strategy for Reducing Wait Times
Bibliographic record
Abstract
PURPOSE: To investigate the effectiveness of a physiotherapy triage service for orthopaedic surgery referrals from primary-care physicians. METHODS: A prospective, observational design was used with patients referred to an orthopaedic surgeon based out of two small urban centres in British Columbia. The level of agreement between the physiotherapist and surgeon was determined using a weighted kappa statistic (κw) with 95% CI. A patient satisfaction questionnaire was administered, and the surgical conversion rate (SCR) was calculated to assess the level of appropriate referrals. RESULTS: The analysis found substantial agreement (κw=0.77; 95% CI, 0.60-0.94) between surgeon and physiotherapist for surgical management decisions. All patients reported being "satisfied" or "very satisfied" with the overall care they received from the physiotherapist. The SCR of patients referred by the physiotherapist to the surgeon was 91%, versus 22% among patients referred by a general practitioner or emergency physician. CONCLUSION: More than three-fourths of patients referred by primary-care physicians did not need to see a surgeon and were able to be managed by an experienced orthopaedic physiotherapist. This triage model could have considerable impact on orthopaedic wait times in Canada by minimizing unnecessary referrals; the model could also promote timely and conservative management of non-surgical conditions by physiotherapists.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".