Risk of Raynaud's Phenomenon Among Workers in the Occupational Disease Surveillance System
Bibliographic record
Abstract
INTRODUCTION: Raynaud's phenomenon (RP) is linked to occupational exposures such as vibration, cold temperature, and chemicals. However, large cohort studies examining RP by occupation and sex are scarce. To address this gap, this study aimed to assess risk of RP by both occupation and sex in a large cohort of workers in Ontario, Canada. METHODS: Workers with accepted lost-time compensation claims were linked to physician billing records to identify diagnoses of RP between 2002 and 2020. A 3-year washout (disease-free) period was applied, and follow-up was limited to 5 years. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for diagnoses of RP, adjusted for age at start of follow-up, birth year, and stratified by sex. RESULTS: A total of 7,131 RP cases were identified among 810,739 workers. Among men, higher risks were observed for truck drivers (HR = 1.23, 95% CI = 1.08-1.41), driver-salesmen (HR = 2.54, 95% CI = 1.21-5.34), those in mining and quarrying-related cutting, handling, and loading (HR = 2.57, 95% CI = 1.29-5.15), and construction trades laboring and elemental work (HR = 1.70, 95% CI = 1.24-2.34). Among women, higher risks were observed for those working in waitressing and related (HR = 1.70, 95% CI = 1.22-2.38), food and beverage preparation (HR = 1.34, 95% CI = 1.02-1.76), and electrical equipment fabricating and assembling (HR 1.96, 95% CI = 1.08-3.55). CONCLUSION: Study findings show elevated risks of RP among various occupations, with notable differences between men and women. These differences may be attributable to variations in potential exposures and susceptibility to RP. Findings underscore the need for large cohort studies to examine RP across various occupational groups and both sexes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".