Risk Factors Associated with Juvenile Idiopathic Arthritis: Exposure to Cigarette Smoke and Air Pollution from Pregnancy to Disease Diagnosis
Bibliographic record
Abstract
Objective. To evaluate exposure to environmental factors inhaled during pregnancy and after birth until juvenile idiopathic arthritis (JIA) diagnosis among residents of a large city. Methods. This is an exploratory case-control study that consists of 66 patients with JIA and 124 healthy controls matched by age and sex, living in the São Paulo, Brazil, metropolitan area until JIA diagnosis, and whose mothers had resided in this region during pregnancy. A structured and reliable questionnaire (κ index for test-retest was 0.80) assessed demographic data, gestational and perinatal-related factors, and exposure to inhalable environmental elements during pregnancy and after birth (occupational exposure to inhalable particles and/or volatile vapor, exposure to cigarette smoke, and the presence of industrial activities or gas stations near the home, work, daycare, or school). Tropospheric pollutants included particulate matter (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), and carbon monoxide (CO). Results. During pregnancy, intrauterine cigarette smoke exposure (OR 3.43, 95% CI 1.45–8.12, p = 0.005) and maternal occupational exposure (OR 13.69, 95% CI 4.4–42.3, p < 0.001) were significant independent risk factors for JIA diagnosis. In contrast, maternal employment (OR 0.06, 95% CI 0.02–0.2, p < 0.001) and ideal maternal weight gain (OR 0.36, 95% CI 0.2–0.8, p = 0.017) presented negative associations. Secondhand smoke exposure from birth to JIA diagnosis (OR 3.6, 95% CI 1.8–7.3, p < 0.001) and exposure to O 3 during the second year of life (OR 2.76, 95% CI 1.20–6.37, p = 0.017) were independent and significant risk factors for the pathogenesis of JIA. Conclusion. In our study, cigarette smoke exposure (intrauterine and after birth), exposure to O 3 in the second year of life, and maternal occupational exposure were identified as potential risk factors for JIA, warranting further study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".