A systematic review and meta‐analysis of environmental factors associated with juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease, thought to be influenced by both genetics and the environment. Identifying environmental factors associated with disease risk will improve knowledge of disease mechanisms and ultimately benefit patients. This review aimed to collate and synthesize the current evidence of environmental factors associated with JIA. METHODS: MEDLINE (Ovid), EMBASE (Ovid), Cumulative Index of Nursing and Related Health Literature (EBSCOhost), science network (WOS, Clarivate Analytics), Chinese National Knowledge Infrastructure, and Chinese Biological Medical Database were systematically searched. Study quality was rated using the Newcastle-Ottawa Scale. Pooled estimates for each environmental factor were generated using a random-effects, inverse-variance method, where possible. The remaining environmental factors were synthesized in narrative form. RESULTS: This review includes environmental factors from 23 studies (6 cohorts and 17 case-control studies). Cesarean section delivery was associated with increased JIA risk (pooled relative risk [RR] 1.103, 95% CI 1.033-1.177). Conversely, maternal smoking of more than 20 cigarettes/day (pooled RR 0.650, 95% CI 0.431-0.981) and gestational smoking (pooled RR0.634, 95% CI 0.452-0.890) were associated with decreased JIA risk. CONCLUSION: This review identifies several environmental factors associated with JIA and demonstrates the huge breadth of environmental research. We also highlight the challenges of combining data collected over this period due to limited study comparability, evolution in healthcare and social practices, and changing environment, which warrant consideration when planning future studies.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".