Impact of compensation on work outcome of carpal tunnel syndrome
Bibliographic record
Abstract
BACKGROUND: Work-related carpal tunnel syndrome (CTS) is a complex and costly condition. There is some evidence that the employment outcome may be worse in cases of CTS where the condition is being considered for compensation. AIM: To examine whether workers' compensation status is an important determinant of outcome of CTS. METHODS: Cases, with a Workers' Compensation Board (WCB) claim, and referents, in work but without a WCB claim, were identified from the practice of a single specialist physician. Data on history prior to and at the time of diagnosis, and events since diagnosis, were collected from clinical records and by a telephone-administered questionnaire. Prior events, severity, treatment and outcome associated with a WCB claim were assessed by logistic regression. RESULTS: Interviews were successfully completed for 46 cases and 50 referents. In the model adjusted only for age and gender, claimants had a worse outcome in terms of changing job or stopping work with time loss from work due to CTS [odds ratio (OR) 5.1, 95% confidence interval (CI) 1.9-13.3]. The OR was much influenced by the inclusion of treatment in the model (OR = 9.6, 95% CI 1.6-58.6) with WCB cases more likely to have surgical and physiotherapy treatments. Cases with a WCB claim cost more to treat and reported greater loss in income than those not seeking compensation. CONCLUSIONS: Although these data are limited, the results are suggestive of poorer outcome among WCB claimants despite greater use of treatment and comparable severity of disease.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".