Early prognostic factors for duration on temporary total benefits in the first year among workers with compensated occupational soft tissue injuries
Bibliographic record
Abstract
AIMS: To develop a model of prognosis for time receiving workers' compensation wage replacement benefits in the first year. METHODS: A prospective cohort of 907 injured workers off work because of soft tissue injuries was followed for one year through structured telephone interviews and administrative data sources. Workers were recruited at workers' compensation claim registration. Only those still off work at four weeks post-registration were included in the analysis. Data from several domains (demographics, clinical factors, workplace factors, recovery expectations) were collected at approximately two weeks and a subset again at four weeks. Outcome was duration on total temporary wage replacement benefits. Variable selection was carried out in two steps using content experts and backward elimination with the Cox model. RESULTS: Body region specific functional status, change in pain, workplace offers of arrangements for return to work, and recovery expectations were independently predictive of time on benefits. Change in pain and workplace offers interacted, so the largest mutual association occurred for those whose pain was getting worse-that is, reduction in median duration from 112.5 to 32.5 days. Across observed values, widely different recovery profiles of groups of workers resulted; for example, at four months, only one third of the highest risk group had gone off benefits while over 95% of the lowest risk group had done so. CONCLUSIONS: Focus on a relatively small set of prognostic factors should enable occupational health practitioners to triage injured workers within the first month and concentrate on those requiring additional assistance to return to work.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".