Factors associated with referral to physiotherapists for adult patients consulting for musculoskeletal disorders in primary care; an ancillary study to ECOGEN
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal disorders (MSD) are multifactorial requiring multidisciplinary treatment including physiotherapy. General practitioners (GP) have a central role in managing MSDs and mostly solicit physiotherapists accounting for 76.1% of physiotherapy referrals in France. Patient, physician, and contextual factors, including healthcare accessibility, can influence physiotherapy referral rates. OBJECTIVE: To identify patient, physician, and contextual factors associated with physiotherapy referral in adult patients with MSDs in general practice. METHODS: This study is based on the 2011/2012 French cross-sectional ECOGEN study. Analyses included working-age patients consulting their GP for any MSD. Physiotherapy referral was assessed initially, then adjusted multilevel logistic model analysis of patient, physician, geographical area-related factors associated with these referrals was performed. RESULTS: Among the 2305 patients included, 456 (19.8%) were referred to a physiotherapist. Following multilevel multivariate analyses, physiotherapist referral was more frequent for female patients (OR 1.28; 95% CI [1.03, 1.59]) with spinal (OR 1.47; 95% CI [1.18, 1.83]) and upper limb disorders (OR 1.66; 95% CI [1.20, 2.29]), and less frequent for patients ≥ 50 years (OR 0.69; 95% CI [0.52, 0.91]), living in deprived geographical areas (OR 0.60; 95% CI [0.40, 0.90]). GPs referred to a physiotherapist less frequently if they were ≥ 50 years (OR 0.50; 95% CI [0.39, 0.63]), had a high number of annual consultations, or were practicing in semi-urban area in a multidisciplinary team. CONCLUSION: This multilevel analysis identifies factors associated with physiotherapy referral for patients with MSDs, including living in deprived geographical areas. This constitutes an original contribution towards addressing healthcare disparities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".