Accuracy of the Örebro Musculoskeletal Pain Questionnaire and Work Assessment Triage Tool for selecting interventions in workers with spinal conditions
Bibliographic record
Abstract
BACKGROUND: Accurate clinical decision support tools may help clinicians select appropriate interventions for patients with spinal conditions. The Örebro Musculoskeletal Pain Questionnaire (ÖMPQ) is a screening questionnaire extensively studied as a predictive tool. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed to help select interventions for injured workers. OBJECTIVE: To compare the classification accuracy of the ÖMPQ and WATT to clinician recommendations for selecting interventions leading to a successful return to work in patients with spinal conditions. METHODS: A secondary analysis was undertaken of data from injured workers with spinal conditions assessed between 2013 and 2016. We considered it a success if the workers did not receive wage replacement benefits 30 days after assessment. Analysis included positive likelihood ratio (LR+) as an indicator of predictive accuracy. RESULTS: Within the database, 2,872 patients had complete data on the ÖMPQ, WATT, and clinician recommendations. At 30 days, the ÖMPQ was most accurate for identifying treatments that lead to successful outcomes with a LR+= 1.51 (95% Confidence Interval 1.26-1.82) compared to 1.05 (95% Confidence Interval 1.02-1.09) for clinicians, and 0.85 (95% Confidence Interval 0.79-0.91) for the WATT. CONCLUSIONS: All tool recommendations had poor accuracy, however the ÖMPQ demonstrated significantly better results.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".