Determinants of work absence following surgery for carpal tunnel syndrome
Bibliographic record
Abstract
BACKGROUND: The objective was to identify factors across multiple domains associated with return to work in a community-based cohort of workers with carpal tunnel syndrome. METHODS: Workers scheduled for carpal tunnel release were recruited into this prospective study. Subjects completed questionnaires preoperatively and at 2, 6, and 12 months postoperatively. The questionnaires contained demographic, clinical, and psychosocial factors and physical and psychosocial workplace stressors. Predictors of work absence at 6 and 12 months were examined in bivariate and multivariate logistic regression analyses. RESULTS: Six months following surgery of 181 subjects, 29 (19%) were out of work. Twelve months postoperatively 33 subjects (22%) were out of work. In bivariate analyses, the factors associated with work absence at 6 months, at P < or = 0.01, included preoperative physical functional status, change in self-efficacy between preoperative assessment and 2 months, lower income, workers' compensation, representation by an attorney, work exposure to force and repetition, higher psychological job demands and lower control, lower social support by coworkers, lower job security and more supportive organizational policies and practices. The factors associated with work absence at 12 months in bivariate analyses included preoperative physical functional status, lower self-efficacy at 2 months, workers' compensation, and less supportive organizational policies and practices. Multivariate analyses documented a multidimensional model, with predictors from multiple domains. CONCLUSIONS: Clinical, demographic, economic, and workplace factors were associated with work absence. Strategies to reduce work absence following carpal tunnel release should address multiple dimensions of the worker and workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".