Occupational and environmental exposures and risk of systemic lupus erythematosus: silica, sunlight, solvents
Bibliographic record
Abstract
OBJECTIVES: We examined occupational and non-occupational exposures in relation to risk of SLE in a case-control study conducted through the Canadian Network for Improved Outcomes in SLE (CaNIOS). METHODS: SLE cases (n = 258) were recruited from 11 rheumatology centres across Canada. Controls (without SLE, n = 263) were randomly selected from phone number listings and matched to cases by age, sex and area of residence. Data were collected using a structured telephone interview. RESULTS: An association was seen with outdoor work in the 12 months preceding diagnosis [odds ratio (OR) 2.0; 95% CI 1.1, 3.8]; effect modification by sun reaction was suggested, with the strongest effect among people who reported reacting to midday sun with a blistering sunburn or a rash (OR 7.9; 95% CI 0.97, 64.7). Relatively strong but imprecise associations were seen with work as an artist working with paints, dyes or developing film (OR 3.9; 95% CI 1.3, 12.3) and work that included applying nail polish or nail applications (OR 10.2; 95% CI 1.3, 81.5). Patients were more likely than controls to report participation in pottery or ceramics work as a leisure activity, with an increased risk among individuals with a total frequency of at least 26 days (OR 2.1; 95% CI 1.1, 3.9). Analyses of potential respirable silica exposures suggested an exposure-response gradient (OR 1.0, 1.4. and 2.1 for zero, one and two or more sources of exposure, respectively; trend test P < 0.01). CONCLUSIONS: This study supports the role of specific occupational and non-occupational exposures in the development of SLE.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".