Risk of Atopic Diseases in Patients with Hidradenitis Suppurativa: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) and allergic diseases were considered to have different immune pathways involved. However, available evidences seemed to be inconsistent and the association was not well-developed yet. OBJECTIVE: The aim of this study was to perform a systematic review and meta-analysis to evaluate the association between HS and atopic diseases, including asthma, atopic dermatitis, allergic rhinitis, and conjunctivitis. METHODS: Search in databases including PubMed, Embase, and Web of Science was performed. Synonyms were determined based on MeSH terms and Emtree. Observational studies with proper comparative arm were selected. For quality evaluation of extracted studies, the Newcastle-Ottawa Scale was utilized. Odds ratio of atopic dermatitis, asthma, allergic rhinitis, and conjunctivitis were evaluated in HS patients (comparing with non-HS people). I2 value was applied to evaluate the heterogeneity within studies. RESULTS: After appraising 1,654 studies, in total, 12 studies were selected for data extraction. In adjusted models, people with HS is significantly associated with higher risk of having asthma, with a pooled odds ratio of 1.50 (95% CI, 1.24-1.81). Risk of presenting atopic dermatitis in HS patients was also increased, with an odd ratio of 4.10 (95% CI, 2.16-8.18). The association remained its significance in sensitivity models. Evidences were insufficient to support the association between HS and allergic rhinitis and conjunctivitis. CONCLUSION: Current evidence supported that atopic dermatitis and asthma were associated with HS. Physicians should be aware of the reported association while caring people with HS and related screening of allergies comorbidities should be recommended.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.008 | 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) | 0.002 | 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".