First return to work following injury: does it reflect a composite or a homogeneous outcome?
Bibliographic record
Abstract
OBJECTIVE: To test whether return to work as a binary (yes/no) outcome that includes all persons who returned to work regardless of mode of return reflects a composite or a homogeneous outcome in a cohort of workers who have sustained acute orthopaedic trauma resulting in hospitalisation. METHODS: Prospective cohort study. One hundred and sixty-eight participants were recruited and followed for 6 months. The study achieved 89% follow-up. Baseline data were obtained at study recruitment and participants were further surveyed by phone at three timepoints during the study. Polytomous logistic regression was used to simultaneously examine the association between potential predictors and different modes of first return to work (RTW). A test of the equality of the ORs associated with the independent predictor variables was also undertaken. RESULTS: Of the 152 participants with full follow-up, 46 (30%) returned first to full duties, 58 (38%) returned first to modified work and 48 (32%) did not return to work during the study period. Significant determinants of the two modes of return to work were different. A test of the equality of ORs indicated that the relative ORs for the difference in the slope coefficients for five of the 10 independent factors in the two polytomous logistic regression sub-models corresponding to each mode of return to work were statistically significant. This raises the likelihood that first RTW reflects a composite rather than a homogeneous outcome. CONCLUSION: The study provides evidence that RTW may reflect a composite outcome when it includes different modes of first RTW. The identified predictive factors appear to exert different mechanisms of action depending on the mode of RTW. The findings suggest that the different modes of RTW may need to be considered independently. The results of the study have potentially important implications for research and insurance practice.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".