Sociodemographic, clinical, and work characteristics associated with return-to-work outcomes following surgery for work-related knee injury
Bibliographic record
Abstract
OBJECTIVE: This study examined the association between return-to-work (RTW) outcomes and sociodemographic, clinical, and work characteristics among a cohort of injured workers who underwent knee surgery between 2001-2005 in British Columbia, Canada. METHODS: Workers' compensation databases were used to identify the retrospective cohort and abstract the study variables. Multinomial logistic regression provided odds ratios (OR) and 95% confidence intervals (95% CI) for the association between RTW (unspecified, partial, full, or non-RTW) and the independent variables. RESULTS: Data was abstracted for 1394 injured workers. Compared to men, women were more likely to have partial RTW (OR 2.55, 95% CI 1.53-4.23) and non-RTW (OR 2.61, 95% CI 1.35-5.03) than full RTW; low income earners were more likely than high income earners to have partial RTW (OR 3.05, 95% CI 1.86-4.99) and non-RTW (OR 4.07, 95% CI 2.19-7.57). Moreover, workers in trade, primary resource, and processing/manufacturing occupations were more likely than those in management occupations to have non-RTW than full RTW by the end of follow-up (OR 2.97, 95% CI 1.32-6.68; OR 9.31, 95% CI 3.41-25.41, and OR 2.71, 95% CI 1.07-6.5, respectively). Surgical and clinical factors were not associated with RTW outcomes. CONCLUSIONS: Using population-based data, our study found a link between sociodemographic and work characteristics and the type of RTW outcome following knee surgery for a work-related injury. Women and lower income earners tended not to have full RTW, after controlling for covariates. Workers in physically demanding occupations also tended not to have full RTW, suggesting that factors beyond clinical and surgical characteristics influence disability outcomes. RTW programs need to take into consideration these broader determinants of worker health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".