Sunlight Exposure, <scp>Sun‐Protective</scp> Behavior, and Anti–Citrullinated Protein Antibody Positivity: A General <scp>Population‐Based</scp> Study in Quebec, Canada
Bibliographic record
Abstract
Objective To examine associations between sunlight exposure and anti–citrullinated protein antibodies (ACPAs) using general population data in Quebec, Canada. Methods A random sample of 7,600 individuals (including 786 subjects who were ACPA positive and 201 self‐reported rheumatoid arthritis [RA] cases) from the CARTaGENE cohort was studied cross‐sectionally. All subjects were nested in 4 census metropolitan areas, and mixed‐effects logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for ACPA positivity related to sunlight exposure, adjusting for sun‐block use, industrial fine particulate matter (PM2.5) exposures, smoking, age, sex, French Canadian ancestry, and family income. We also performed sensitivity analyses excluding subjects with RA, defining ACPA positivity by higher titers, and stratifying by age and sex. Results The adjusted ORs and 95% CIs did not suggest conclusive associations between ACPA and sunlight exposure or sun‐block use, but robust positive relationships were observed between industrial PM2.5 emissions and ACPA (OR 1.19 per μg/m3 [95% CI 1.03–1.36] in primary analyses). Conclusion We did not see clear links between ACPA and sunlight exposure or sun‐block use, but we did note positive associations with industrial PM2.5. Future studies of sunlight and RA (or ACPA) should take air pollution exposures into account.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".