Predictors of return to work following traumatic work-related lower extremity amputation
Bibliographic record
Abstract
PURPOSE: To determine factors predictive of return to work (RTW) and days of total disability (TD) in a population of persons working at the time of lower extremity amputation. METHOD: Retrospective chart and database review. RESULTS: Of 88 valid cases, 48% involved toe amputation, 23% transtibial, 14% partial foot, 14% transfemoral, and 2% high level. Fifty-eight percent of all subjects RTW, 19% were deemed 'fit for work', and 23% did not RTW. Days TD ranged from 0 to 1664, with a mean of 366 days. Toe amputation level showed a mean of 127 days of TD. Bivariate analysis showed amputation level, total costs to Workers Compensation Board (WCB), and days TD significantly related to RTW, and rehabilitation costs, vocational rehabilitation, work assessment, age, number of surgical procedures, number of days in acute care, and amputation level significantly related to days TD. In the multivariate model, only amputation level and higher gross annual income showed predictive value for RTW. However older age, more surgical procedures, less days in hospital, and higher amputation levels were all predictive of increased days TD. CONCLUSION: Toe amputation level had a surprisingly high number of days TD, which may have significant potential economic and disability impact on the workplace. Other factors beyond simply amputation level (such as previous income level) are important considerations for RTW.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".