A Spinal Triage Programme Delivered by Physiotherapists in Collaboration with Orthopaedic Surgeons
Bibliographic record
Abstract
PURPOSE: To describe the characteristics of participants in a physiotherapist spinal triage programme, compare the profiles of patients for whom surgery was and was not recommended by a surgeon, and determine the surgical yield among those referred to surgeons. METHODS: Data were collected retrospectively by reviewing charts of people who used the service over a 3-year period (2003-2006). Data from up to1,096 people were used in the analysis; complete data were available for 299 people. Descriptive statistics were used to summarize demographics, clinical features, and management recommendations. Characteristics of those who were and were not recommended for surgery were examined using Pearson's chi-square or Fisher's Exact tests. RESULTS: The majority of 746 participants were classified as "mechanical spine" (92.5%), 2.9% were "other body part," 2.5% were "medical/other," and only 2% were classified as "surgical spine." Recommendations for surgery (by a surgeon) were independent of patients' age, sex, duration of symptoms, residence (urban/rural), source of health care funding, and diagnosis. The surgical yield was 80%. CONCLUSIONS: Most people were not considered candidates for surgery. Triage assessment by physiotherapists can increase the efficiency of an orthopaedic surgeon's caseload by reducing the number of non-surgical referrals and can thus help to ensure more timely access to appropriate health care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".