The role of bacterial skin infections in atopic dermatitis: expert statement and review from the International Eczema Council Skin Infection Group
Bibliographic record
Abstract
Patients with atopic dermatitis (AD) have an increased risk of bacterial skin infections, which cause significant morbidity and, if untreated, may become systemic. Staphylococcus aureus colonizes the skin of most patients with AD and is the most common organism to cause infections. Overt bacterial infection is easily recognized by the appearance of weeping lesions, honey-coloured crusts and pustules. However, the wide variability in clinical presentation of bacterial infection in AD and the inherent features of AD - cutaneous erythema and warmth, oozing associated with oedema, and regional lymphadenopathy - overlap with those of infection, making clinical diagnosis challenging. Furthermore, some features may be masked because of anatomical site- and skin-type-specific features, and the high frequency of S. aureus colonization in AD makes positive skin swab culture of suspected infection unreliable as a diagnostic tool. The host mechanisms and microbial virulence factors that underlie S. aureus colonization and infection in AD are incompletely understood. The aim of this article is to present the latest evidence from animal and human studies, including recent microbiome research, to define the clinical features of bacterial infections in AD, and to summarize our current understanding of the host and bacterial factors that influence microbial colonization and virulence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".