<i>Malassezia</i> specific IgE in head and neck dermatitis of eczema: A systematic review & meta‐analysis
Bibliographic record
Abstract
Head and neck atopic dermatitis (HNAD) is a subtype of atopic dermatitis (AD), a common inflammatory skin condition with a distinctive clinical appearance. Malassezia spp., a predominant skin yeast, is considered to exacerbate HNAD. In this study, we investigate the prevalence of Malassezia-specific IgE among HNAD patients. A comprehensive search was performed for observational studies analysing the association between Malassezia-specific IgE and HNAD. This study was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 checklist and quality was assessed via the Newcastle-Ottawa Quality Assessment Scale (NOS). Fourteen observational studies (840 patients) were included in the analysis. 58% of HNAD patients were male (95% CI: 45.2-69.7). Overall prevalence of Malassezia-specific IgE among HNAD patients was 79.3% (95% CI: 57.5-91.5). Prevalence of Malassezia-specific IgE among HNAD patients varied significantly between geographical regions (p = 0.0441), with 88% in non-Asian regions (95% CI: 61.06-97.17) and 54.73% in Asian regions (95% CI: 34.36-73.63). Malassezia-specific IgE prevalence among HNAD patients varied significantly among studies of higher and lower NOS quality score (p = 0.0386), with 95.42% in studies with NOS ≥7 (95% CI: 63.54-99.60) and 58.05% in studies with NOS <7 (95% CI: 41.44-73.01). Malassezia-specific IgE prevalence among HNAD patients did not vary significantly between more and less predominant Malassezia species (p = 0.1048). Malassezia spp. plays a crucial role in the pathogenesis of HNAD, and IgE anti-Malassezia antibodies appeared to be a common marker for HNAD. Understanding the pathophysiology of Malassezia in HNAD can help develop more targeted therapeutic approaches in managing AD.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".