Evaluation of an Advanced-Practice Physical Therapist in a Specialty Shoulder Clinic: Diagnostic Agreement and Effect on Wait Times
Bibliographic record
Abstract
PURPOSE: To examine the role of an advanced-practice physiotherapist (APP) with respect to (1) agreement with an orthopaedic surgeon on diagnosis and management of patients with shoulder problems; (2) wait times; and (3) satisfaction with care. METHODS: This prospective study involved patients with shoulder complaints who were referred to a shoulder specialist in a tertiary care centre. Agreement was examined on seven major diagnostic categories, need for further examination and surgery, and type of surgical procedure. Wait times were compared between the APP- and surgeon-led clinics from referral date to date of initial consultation, date of final diagnostic test, and date of confirmed diagnosis and planned treatment. A modified and validated version of the Visit-Specific Satisfaction Instrument assessed satisfaction in seven domains. Kappa (κ) coefficients and bias- and prevalence-adjusted kappa (PABAK) values were calculated, and strength of agreement was categorized. Wait time and satisfaction data were examined using non-parametric statistics. RESULTS: Agreement on major diagnostic categories varied from 0.68 (good) to 0.96 (excellent). Agreement with respect to indication for surgery was κ=0.75, p<0.001; 95% CI, 0.62-0.88 (good). Wait time for APP assessment was significantly shorter than wait time for surgeon consultation at all time points (p<0.001); the surgeon's wait time was significantly reduced over 3 years. High satisfaction was reported in all components of care received from both health care providers. CONCLUSIONS: Using experienced physiotherapists in an extended role reduces wait times without compromising patient clinical management and overall satisfaction.
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".