Association of Adult Atopic Dermatitis Severity with Bacterial, Viral, and Fungal Skin Infections
Bibliographic record
Abstract
Abstract: Background: Little is known about the relationship of atopic dermatitis (AD) severity, phenotype, and persistence on different types of skin infections. Objective: To evaluate the relationship of AD characteristics and skin infections over time in adults. Methods: We performed a prospective dermatology practice-based study (n = 559). History of infection was assessed using questionnaires. AD severity was evaluated using Scoring Atopic Dermatitis (SCORAD), Eczema Area and Severity Index (EASI), Investigator's Global Assessment (IGA), and Patient-reported Global Assessment (PtGA). Results: At baseline, 160 (21.4%) patients reported history of ≥1 skin infection, including 14.3% with bacterial infections. In multivariable repeated measures logistic regression models, ≥1 cutaneous infection was associated with moderate (adjusted odds ratio [95% confidence interval]: 2.67 [1.67–4.28]) and severe (6.35 [3.36–12.01]) versus mild SCORAD; as well as severe SCORAD-itch; moderate and severe versus clear-mild EASI; moderate and severe versus clear-mild PtGA; mild, moderate, and severe versus clear-almost clear IGA. Cutaneous infections were not associated with ichthyosis, palmar hyperlinearity, nummular eczema, cheilitis, or hand eczema. Specific infections varied by AD severity and body site. Persistent moderate–severe disease was associated with higher odds of skin infection. Conclusion: Skin infections were associated with AD severity but not phenotype, and may be mitigated by improved AD severity.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".