Evaluation of an advanced-practice physiotherapist in triaging patients with lumbar spine pain: surgeon–physiotherapist level of agreement and patient satisfaction
Bibliographic record
Abstract
BACKGROUND: Surgery for lumbar spine pain is indicated for specific etiologies. Given the majority of individuals referred to spine surgeons are not surgical candidates, care delivery is inefficient, with consultations being of limited value for most. Using specially trained physiotherapists in triage is a human resource strategy that may optimize surgeons' time and the patient experience. METHODS: An advanced-practice physiotherapist (APP) and a surgeon assessed consecutive patients with lumbar spine pain presenting at an academic health centre's spine surgery clinic. The second assessor was blinded to the outcome of the first. We used the κ statistic to evaluate surgeon-APP level of chance-corrected agreement concerning patients' need for a surgical consultation. To assess satisfaction with the APP, patients completed a modified version of the validated Visit-specific Questionnaire. RESULTS: The sample included 102 participants (54 women) with a mean age of 54.3 ± 14.3 years and a mean Oswestry Disability Index score of 35.4 ± 16.6. The assessors' overall agreement was 86%. The κ coefficient for the need for a surgical consultation was 0.69 (95% confidence interval 0.54-0.84). The APP identified that 77% of patients did not require a surgical consultation. Twenty-one patients underwent surgery. Satisfaction scores for the APP were very high (mean score 92 out of 100). CONCLUSION: In triaging patients with lumbar spine pain, the APP and surgeon had a high level of agreement. An APP performing triage at a surgical centre can effectively reduce wait lists by 70%, reserving surgical consultations for those patients in whom they are indicated.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".