Atopic Dermatitis is a Risk Factor for Rheumatoid Arthritis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: It is still unclear whether patients with atopic dermatitis (AD) have an increased risk of developing rheumatoid arthritis (RA). OBJECTIVE: We aimed to investigate the association between AD and risk of RA using systematic review and meta-analysis. METHODS: We searched Medline and EMBASE up to April 2021 using search strategy, including terms for "atopic dermatitis" and "rheumatoid arthritis." Eligible cohort study must compare the incidence of RA between patients with AD and comparators without AD. Eligible case-control study must recruit cases with RA and controls without RA. Then, the study must compare the prevalence of AD between the groups. Point estimates with standard errors from each study were combined using the generic inverse variance method. RESULTS: The meta-analysis found that AD patients had a significantly higher risk of incident RA than individuals without AD with a pooled odds ratio (OR) of 1.30 (95% confidence interval [CI], 1.17-1.44; I2, 48%). Subgroup analysis revealed a significantly higher risk of RA in cohort study subgroup (pooled OR, 1.37; 95% CI, 1.25-1.50; I2, 63%) but not case-control study subgroup (pooled OR, 0.99; 95% CI, 0.77-1.28; I2, 10%). CONCLUSIONS: This study found a significantly higher risk of incident RA among AD patients.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.003 | 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".