Atopic dermatitis and its relation to food allergy
Bibliographic record
Abstract
PURPOSE OF REVIEW: To present the most recent evidence on atopic dermatitis and its relation to food allergy. RECENT FINDINGS: Atopic dermatitis is a chronic inflammatory disorder of the skin characterized by impaired skin barrier because of multifactorial causes including genetic factors, immune dysregulation, and skin microbiome dysbiosis. Infants with temporary skin barrier disruption and/or persistent atopic dermatitis are particularly at risk of developing food allergy (during the so-called atopic march), with up to half of patients demonstrating positive food-specific IgE and one-third of severe cases of atopic dermatitis having positive symptoms on oral food challenge. A high proportion of children with atopic dermatitis exhibit asymptomatic sensitization to foods, and skin testing to identify potential food triggers is not recommended unless the patient has a history suggestive of food allergy and/or moderate-to-severe atopic dermatitis unresponsive to optimal topical care. Indeed, indiscriminate testing can lead to a high proportion of false-positive tests and harmful dietary evictions. Promising strategies to prevent food allergy in children with atopic dermatitis include early skincare with emollients and treatment with topical steroid, and early introduction of highly allergenic foods. SUMMARY: Further studies are required to identify risk factors for atopic dermatitis to help prevent the development of food allergy in this high-risk population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".