Association of Atopic Dermatitis with Bacterial, Fungal, Viral, and Sexually Transmitted Skin Infections
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is associated with altered skin barrier, microbiome, and immune dysregulation that may increase risk of skin infections. OBJECTIVE: The aim of the study was to determine whether AD is associated with skin infections and related outcomes. METHODS: Data from the 2006 to 2012 National Emergency Department Sample were analyzed, including an approximately 20% sample of all US emergency department (ED) visits (N = 198,102,435 adults or children). RESULTS: Skin infections were increased in ED visits of adults (7.14% vs 3.76%) and children (5.15% vs 2.48%) with AD. In multivariable logistic regression models, AD was associated with significantly higher odds of skin infection in adults (adjusted odds ratio [95% confidence interval] = 1.93 [1.89-1.97]) and children (2.23 [2.16-2.31]). Pediatric and adult AD were associated with significantly higher odds of carbuncle/furuncles, impetigo, cellulitis, erysipelas, methicillin-resistant and methicillin-sensitive Staphylococcus aureus infections, molluscum contagiosum, cutaneous warts, herpes simplex and zoster viruses, eczema herpeticum, dermatophytosis, and candidiasis of skin/nails and vulva/urogenitals. Adults with AD had significantly higher odds of genital warts (1.51 [1.36-1.52]) and herpes (1.23 [1.11-1.35]). Skin infections were associated with US $19 million excess annual costs of ED care in persons with AD. CONCLUSIONS: Atopic dermatitis patients had higher odds of multiple bacterial, viral, fungal, and sexually transmitted skin infections.
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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 | 0.001 |
| 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".