Occupational Exposure to Whole-Body Vibration and Parkinson's Disease: Results From a Population-based Case-Control Study
Bibliographic record
Abstract
Mechanical stress producing head injury is associated with Parkinson's disease, suggesting that relations with other physical hazards such as whole-body vibration (WBV) should be tested. In this study, the authors evaluated the relation between occupational exposure to WBV and Parkinson's disease. A population-based case-control study with 403 cases and 405 controls was conducted in British Columbia, Canada, between 2001 and 2008. From detailed occupational histories and published measurements, metrics of occupational WBV exposure were constructed and tested for associations with Parkinson's disease using logistic regression and adjusting for age and sex first, and then also for smoking and history of head injury. While ever being occupationally exposed to WBV was inversely associated with Parkinson's disease (odds ratio = 0.67, 95% confidence interval: 0.48, 0.94), higher intensities had consistently elevated odds ratios, with a statistically significant effect being noted for intermediate intensities when exposures were restricted to the 10 years or more prior to diagnosis. Possible mechanisms of an inverse relation between low levels of WBV exposure and Parkinson's disease could include direct protective effects or correlation with other protective effects such as exercise. Higher intensities of WBV could result in micro-injury, leading to vascular or inflammatory pathology in susceptible neurons.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".