Predicting Treatment Failure in the Subacute Injury Phase Using the ??rebro Musculoskeletal Pain Questionnaire: An Observational Prospective Study in a Workers??? Compensation System
Bibliographic record
Abstract
OBJECTIVE: The goal of the present study was to examine if patient scores on a brief biopsychosocial screening questionnaire--the Orebro Musculoskeletal Pain Questionnaire (OMPQ)--could predict clinical discharge status ("fit" vs "not fit" for return to work) after a standardized 6-week physical therapy-based work conditioning program. METHODS: The OMPQ was administered to a derivation sample of 200 injured workers with soft tissue injuries before beginning treatment. A clinical cutoff score of 147 was subsequently tested in a second validation sample of 211 injured workers. RESULTS: The OMPQ was able to correctly predict the discharge status of 85% of claimants. CONCLUSIONS: These results suggest that the OMPQ can facilitate clinical decision-making through early identification of individuals likely to fail a unidisciplinary physical therapy program and who may benefit from more complete biopsychosocial treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".