Advanced practice physiotherapy surgical triage and management of adults with spinal disorders referred to specialized spine medical care: a retrospective observational study
Bibliographic record
Abstract
Introduction In this novel advanced practice physiotherapy (APP) model of care, advanced practice physiotherapists (APPTs) assess, triage, and manage adults with spinal disorders to alleviate the growing demands in specialized spine medical care.Objectives To describe this APP model of care, to assess change in disability 3 months after rehabilitation care and to assess surgical triage and diagnostic concordance between APPTs and spine surgeons.Methods In this retrospective observational study, consecutive patients who completed the 3-month follow-up data were analyzed. Sociodemographic, clinical characteristics, and self-reported disabilities including the Oswestry Disability Index (ODI) and Neck Disability Index (NDI) at baseline and 3 months were extracted. Paired t-tests were used to assess changes in disability. Surgical triage and diagnostic concordance between APPTs and surgeons were measured with raw agreement, Cohen’s Kappa, and PABAK.Results In this model, trained APPTs triaged surgical candidates and provided rehabilitation care including education and exercises to patients with spinal disorders. The APPTs referred only 18/46 participants to spine surgeons. Surgical triage and diagnostic concordance were high with raw agreement of 94% and 89%. At the 3-month follow-up, significant improvements in disability were observed among nonsurgical candidates with back (mean difference (MD): −13.0/100 [95%CI: −19.8 to −6.3], n = 23) or neck disorders (MD: −16.0/100 [95%CI: −29.6 to −2.4], n = 5), but not among surgical candidates referred by APPTs to spine surgeons.Conclusion In this limited sample, adults with spinal disorders that were initially referred to a spine surgeon by family physicians were effectively assessed, triaged, and managed by an APPT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".