Prevalence and odds of Staphylococcus aureus carriage in atopic dermatitis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Staphylococcus aureus is increasingly implicated as a possible causal factor in the pathogenesis of atopic dermatitis (AD). However, the reported prevalence rates of skin and nasal colonization in the literature vary widely. Objectives This study evaluates the prevalence and odds of skin and nasal colonization with S. aureus in patients with AD. Methods A systematic literature search was conducted. Odds ratios (ORs) for colonization in patients vs. controls and the prevalence of colonization in patients were pooled using the random-effects model. Results Overall, 95 observational studies were included, of which 30 had a control group. The Newcastle-Ottawa Scale was used to assess study quality, with the majority of studies being of fair to poor quality. Patients with AD were more likely to be colonized with S. aureus than healthy controls [OR 19 +/- 74, 95% confidence interval (CI) 10.88-35.81]. Differences were smaller in nonlesional skin (OR 7.77, 95% CI 3.82-15.82) and in the nose (OR 4.50, 95% CI 3.00-6.75). The pooled prevalence of S. aureus colonization among patients was 70% for lesional skin, 39% for nonlesional skin and 62% for the nose. In lesional skin, meta-regression showed that the prevalence of colonization increased with disease severity. Study heterogeneity should be taken into consideration when interpreting the results. Conclusions These results demonstrate the importance of colonization with S. aureus in AD. Further evaluation of the mechanisms by which S. aureus influences inflammation is required in addition to the development of targeted strategies to decrease skin and nasal S. aureus load.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 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) | 0.003 | 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".