Exposure to hand-arm vibrations in the workplace and the occurrence of hand-arm vibration syndrome, Dupuytren’s contracture, and hypothenar hammer syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
This study provides an overview of the relationships between exposure to work-related hand-arm vibration and the occurrence of pre-defined disorders of the hands. We searched Medline, Embase, Web of Science, Cochrane Central, and PsycINFO for cross-sectional and longitudinal studies on the association between work-related vibration exposure and the occurrence of hand-arm vibration syndrome (including vibration-induced white finger), Dupuytren's contracture, or hypothenar hammer syndrome. We used a 16-item checklist for assessing the risk of bias. We present results narratively, and we conducted random effects meta-analyses if possible. We included 10 studies with more than 24,381 participants. Our results showed statistically significant associations between the exposure to hand-arm vibrations and the occurrence of the selected disorders, with pooled odds ratios ranging between 1.35 (95% CI: 1.28 to 2.80) and 3.43 (95% CI: 2.10 to 5.59). Considerable between-study heterogeneity was observed. Our analyses show that exposure to vibrating tools at work is associated with an increased risk for the occurrence of selected disorders of the hands. Due to the majority of studies being cross-sectional, no firm conclusion is possible regarding causal relationships between vibration exposure and disorder occurrence. Future research should specifically address whether reducing exposure to hand-held vibrating tools at work reduces the incidence of the disorders of the hands investigated in this systematic review.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".