Are Components of a Comprehensive Medical Assessment Predictive of Work Disability After an Episode of Occupational Low Back Trouble?
Bibliographic record
Abstract
STUDY DESIGN: One hundred fifty-nine subacute low back work-injured patients completed a full medical assessment at baseline. A full repeat examination was performed 3 months later, when return-to-work status was determined. OBJECTIVE: To determine the prognostic value of a comprehensive medical assessment for the prediction of return-to-work status. SUMMARY OF BACKGROUND DATA: A systematic review of the work disability prediction literature of low back trouble prognosis revealed that no high-quality studies included a full medical history and physical examination in the design. The results of studies included in the systematic review were equivocal with respect to predictive usefulness of medical variables. METHODS: Participants completed medical history questionnaires and then were clinically examined by one of six experienced examiners (three physicians and three physiotherapists). Return-to-work status was measured 3 months later, and predictive validity was evaluated using logistic regression modeling. RESULTS: Medical variables (, medical history subscales, physical examination subscales, and lumbar range-of-motion tests) showed modest correct classification rates varying between 61.6% and 69.1% for participants. CONCLUSIONS: Comprehensive medical assessments play a crucial role in the early identification of serious pathology after low back trouble. We were unable to identify, however, any medical evaluation variables that would account for significant proportions of variance in return to work. The weight of evidence obtained in this study suggests that injured workers' subjective interpretations and appraisals may be more powerful predictors of the course of postinjury recovery than exclusively medical assessments.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".