Influence of compensation status on time off work after carpal tunnel release and rotator cuff surgery: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The assessment of post-surgical outcomes among patients with Workers' Compensation is challenging as their results are typically worse compared to those who do not receive this compensation. These patients' time to return to work is a relevant outcome measure as it illustrates the economic and social implications of this phenomenon. In this meta-analysis we aimed to assess the influence of this factor, comparing compensated and non-compensated patients. FINDINGS: Two authors independently searched MEDLINE (Ovid), Embase (Ovid), CINAHL, Google Scholar, LILACS and the Cochrane Library and also searched for references from the retrieved studies. We aimed to find prospective studies that compared carpal tunnel release and elective rotator cuff surgery outcomes for Workers' Compensation patients versus their non-compensated counterparts. We assessed the studies' quality using the Guyatt & Busse Risk of Bias Tool. Data collection was performed to depict included studies characteristics and meta-analysis. Three studies were included in the review. Two of these studies assessed the outcomes following carpal tunnel release while the other focused on rotator cuff repair. The results demonstrated that time to return to work was longer for patients that were compensated and that there was a strong association between this outcome and compensation status - Standard Mean Difference, 1.35 (IC 95%; 0.91-1.80, p < 0.001). CONCLUSIONS: This study demonstrated that compensated patients have a longer return to work time following carpal tunnel release and elective rotator cuff surgery, compared to patients who did not receive compensation. Surgeons and health providers should be mindful of this phenomenon when evaluating the prognosis of a surgery for a patient receiving compensation for their condition. TYPE OF STUDY/LEVEL OF EVIDENCE: Meta-analysis of prospective Studies/ Level III.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".