A physiotherapy triage assessment service for people with low back disorders: evaluation of short-term outcomes
Bibliographic record
Abstract
PURPOSE: To determine the short-term effects of physiotherapy triage assessments on self-reported pain, functioning, and general well-being and quality of life in people with low back-related disorders. METHODS: Participants with low back-related complaints were recruited from those referred to a spinal triage assessment program delivered by physiotherapists (PTs). Before undergoing the triage assessment, the participants completed a battery of questionnaires covering a range of sociodemographic, clinical, and psychosocial features. The study used the Numeric Pain Rating Scale (NPRS), the Oswestry Disability Index (ODI), and the Medical Outcomes Survey 36-item short-form version 2 (SF-36v2) to assess self-reported pain, function, and quality of life. Baseline measures and variables were analyzed using a descriptive analysis method (ie, proportions, means, medians). Paired samples t-tests or Wilcoxon matched-pair signed-rank tests were used to analyze the overall group differences between the pretest and posttest outcome measures where appropriate. RESULTS: A total of 108 out of 115 (93.9%) participants completed the posttest survey. The Physical Component Summary of the SF36v2 was the only measure that demonstrated significant improvement (P < 0.001). CONCLUSION: A spinal triage assessment program delivered by PTs can be viewed as a complex intervention that may have the potential to affect a wide range of patient-related outcomes. Further research is needed to examine the long-term outcomes and explore potential mechanisms of improvement using a biopsychosocial framework.
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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 | 0.004 | 0.009 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".