Determining the Impact of a Physiotherapist-Led Primary Care Model for Hip and Knee Pain: Protocol and Analysis Plan for a Cluster Randomized Controlled Trial With Process Evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Hip and knee pain are leading contributors to disability, reduced quality of life, and health care burden in Canada. Primary care is often the first point of contact for patients with these conditions, yet timely and appropriate care is limited due to provider shortages and system pressures. Interest is growing in interprofessional primary care models that integrate physiotherapists to enhance care delivery for musculoskeletal conditions such as hip and knee pain. OBJECTIVE This study aims to communicate the protocol and analysis plan for a trial with two objectives: (1) to evaluate the effectiveness of a physiotherapist-led primary care model for hip and knee pain on physical functioning (primary outcome), pain intensity, quality of life, global rating of change, patient satisfaction, and adverse events, compared to usual physician-led primary care; and (2) to assess the impact of this model on health care system and societal outcomes, including access to care, health care use, productivity loss, and cost-effectiveness. A process evaluation will examine implementation, potential mechanisms, and patient experiences. METHODS A cluster randomized controlled trial involving 14 primary care organizations randomized equally to either a physiotherapist-led or usual physician-led primary care model for patients with hip and knee pain. Patients were recruited over 1 year, with data collected at baseline and at 3, 6, 9, and 12 months. The intervention integrates a physiotherapist as the initial point of contact within the primary care team for patients seeking care for hip or knee pain. It includes four components: (1) comprehensive assessment and screening, (2) brief individualized interventions during the initial visit, (3) guidance for accessing additional health care resources, and (4) follow-up physiotherapy for patients with unmet needs. Effectiveness will be assessed using linear mixed regression, accounting for clusters and prespecified covariates. The multimethods process evaluation will include descriptive and comparative analysis of implementation, mediation analysis to explore potential mechanisms, and qualitative exploration of patient experiences. RESULTS This research was funded in December 2022. Primary care sites (clusters) were recruited and randomized in June and July 2023, respectively. Patient enrollment occurred from October 2023 through November 2024. The final patient follow-up survey was completed in November 2025. Extraction of data from electronic health records is expected to finish in December 2025. Data analysis will begin after data collection is complete and will follow the predefined protocol and analysis plan. No interim analyses are planned. CONCLUSIONS Findings from this trial will provide actionable evidence on whether integrating physiotherapists into primary care teams for hip and knee pain improves patient outcomes and health care system efficiency. Effectiveness and process evaluation evidence will inform policymakers and health system leaders on the adoption and implementation of interprofessional, team-based primary care models. CLINICALTRIAL ClinicalTrials.gov NCT06358521; https://clinicaltrials.gov/study/NCT06358521 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/89006
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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 | 0.090 | 0.105 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".