Factors Associated with Return to Work Following Work-Related Injuries to the Lower Extremities
Bibliographic record
Abstract
Purpose: To identify factors associated with return-to-work (RTW) following work-related foot and ankle injuries.Methods: 86 patients with work-related foot and ankle injuries were asked to complete questionnaires during a comprehensive assessment at entry to a treatment program, at discharge, and at three months' post-treatment (P-T) follow-up.The primary study outcome was RTW status at 3 months P-T follow-up.The relationship between RTW status at 3 months PT follow-up was modelled against the independent variables of age, time since injury, as well as Lower Extremity Functional Scale score (LEFS) at initial presentation, using logistic regression.The secondary study outcome was RTW status and predictors of RTW at discharge. Results:The overall RTW rate at 3 months P-T follow-up in the patients with work-related foot and ankle injuries was 33.7%.There were no significant demographic differences between the patients who were able to RTW at 3 months P-T follow-up and those that did not.In a logistic regression model, a greater time since injury was a significant predictor of being less likely to RTW at 3 months P-T follow-up (OR= 0.527, 95%CI [0.295, 0.940]).Similar results were obtained for patients able to RTW at discharge.Conclusion: Time since injury is the strongest predictor of RTW at 3 months P-T in patients suffering from work-related foot and ankle injuries.Placing emphasis on early referrals to treatment may improve the RTW rates for these injured workers.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".