Factors That Predict Remission of Infant Atopic Dermatitis: A Systematic Review
Bibliographic record
Abstract
The individual prognosis of infants with atopic dermatitis (AD) is important for parents, healthcare professionals, and society. The aim of this study was to investigate predictors for remission of infant AD until school age. A systematic review was carried out of clinical and epidemiological studies investigating the effect of filaggrin gene (FLG) loss-of-function mutations, sex, exposure to pets, topical anti-inflammatory treatment, disease severity, and atopic sensitization during infancy on complete remission of infant-onset AD until 6-7 years of age. Systematic electronic searches until September 2013, data abstraction, and study quality assessment (Newcastle-Ottawa Scale) were performed. From 3,316 abstracts identified, 2 studies of good study quality were included. Parental allergies and sex did not significantly affect remission. For non-remission of AD, the included articles reported an association with any atopic sensitization at 2 years old (adjusted odds ratio [aOR] 2.76; 95% confidence interval (CI) 1.29-5.91), frequent scratching with early AD (aOR 5.86; 95% CI 3.04-11.29), objective severity score at 2 years old (aOR 1.10; 95% CI 1.07-1.14), and exposure to pets (cat OR 2.33; 95% CI 0.85-6.38). It is largely unknown which factors predict remission of infant AD. This is a highly relevant research gap that hinders patient information on the prognosis of infant-onset 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".