Supplementary Material for: The Bidirectional Association between Inflammatory Bowel Disease and Atopic Dermatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Previous investigations have suggested a significant association between inflammatory bowel disease (IBD) and atopic dermatitis (AD). Yet, outcomes published remain inconsistent. Objective: To explore the association between IBD and AD by a systematic review and meta-analysis. Methods: A comprehensive search of studies published from March 1, 1968, to July 26, 2019, was performed in electronic databases as follows: PubMed, Embase, Cochrane Library, and Web of Science. Methodological quality was assessed based on the Newcastle-Ottawa Scale. Data analysis was conducted using R version 3.6.1 (meta package version 4.9-7). Results: A total of 14 studies were eligible for exploring the association between IBD and AD. Statistically significant differences were found on the risk of AD comorbidity among patients with IBD (risk ratio [RR] 1.83, 95% CI 1.39–2.40), Crohn’s disease (CD; RR 2.06, 95% CI 1.61–2.64), and ulcerative colitis (UC; RR 1.66, 95% CI 1.23–2.24). Compared with non-AD subjects, patients with AD were 48% (p = 0.019), 44% (p = 0.002), and 38% (p = 0.000) more likely to exhibit IBD, CD as well as UC, respectively. Discussion: Our evidence supported a significant bidirectional association between IBD and AD. Future prospective studies are warranted to explore underlying mechanisms linking them.
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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 | 0.006 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.716 | 0.044 |
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".