Vibration induced white-feet: Overview and field study of vibration exposure and reported symptoms in workers
Bibliographic record
Abstract
BACKGROUND: Workers who stand on platforms or equipment that vibrate are exposed to foot-transmitted vibration (FTV). Exposure to FTV can lead to vibration white feet/toes resulting in blanching of the toes, and tingling and numbness in the feet and toes. OBJECTIVES: The objectives are 1) to review the current state of knowledge of the health risks associated with foot-transmitted vibration (FTV), and 2) to identify the characteristics of FTV and discuss the associated risk of vibration-induced injury. PARTICIPANTS: Workers who operated locomotives (n=3), bolting platforms (n=10), jumbo drills (n=7), raise drilling platforms (n=4), and crushers (n=3), participated. METHODS: A tri-axial accelerometer was used to measure FTV in accordance with ISO 2631-1 guidelines. Frequency-weighted root-mean-square acceleration and the dominant frequency are reported. Participants were also asked to report pain/ache/discomfort in the hands and/or feet. RESULTS: Reports of pain/discomfort/ache were highest in raise platform workers and jumbo drill operators who were exposed to FTV in the 40 Hz and 28 Hz range respectively. Reports of discomfort/ache/pain were lowest in the locomotive and crusher operators who were exposed to FTV below 10 Hz. These findings are consistent with animal studies that have shown vascular and neural damage in exposed appendages occurs at frequencies above 40 Hz. CONCLUSIONS: Operators exposed to FTV at 40 Hz appear to be at greater risk of experiencing vibration induced injury. Future research is required to document the characteristics of FTV and epidemiological evidence is required to link exposure with injury.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".