Guide to the Effects of Vibration on Health—Quantitative or Qualitative Occupational Health and Safety Prevention Guidance? A Scoping Review
Bibliographic record
Abstract
This systematic review examined the health risk assessment methods of studies of whole-body vibration exposure from occupational vehicles or machines utilizing the International Standard ISO 2631-1 (1997) and/or the European Machine Directive 2002/44. This review found inconsistent reporting of measurement parameters in studies on whole-body vibration (WBV) exposure. Although many authors treat the ISO 2631-1 HGCZ as a medical health standard with defined threshold levels, the epidemiological evidence for these limits is unclear. Similarly, the EU Directive offers more comprehensive risk management guidance, but the numeric limits are equal without supporting scientific evidence. Both guidelines likely represent the prevailing societal and interdisciplinary consensus at the time. Authors note discrepancies between international and national standards and adverse WBV exposure outcomes are reported below given boundaries. Future publications should report all relevant parameters from ISO 2631-1 and clearly state study limitations, exercising caution when applying ISO 2631-1 HGCZ in health and safety assessments and considering different susceptibility of diverse populations. We advise reducing WBV exposure to the lowest technically feasible limits wherever possible and applying the precautionary principle with attention to individual differences, instead of depending solely on numeric limits.
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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 | 0.040 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".