Skin‐centered strategies in food allergy prevention
Bibliographic record
Abstract
While the early introduction of food allergens in the infant diet has been shown to be effective at preventing the development of food allergy (FA), its implementation in real life has been associated with various challenges. Interventions aimed at correcting skin barrier dysfunction have been explored in recent decades as a distinct or complementary mean to prevent allergic sensitization through the skin and subsequent development of FA. Studies assessing the application of emollient from birth have yielded conflicting results, and meta-analyses have demonstrated either no effect or only a slight positive effect on FA prevention. However, a careful review of the clinical trials reveals that different emollients were used, which may have explained some of the discrepancies between study results. Emollient application protocols also varied widely between studies. While firm conclusions cannot be drawn with regard to their overall efficacy at preventing FA, the available data provide valuable insight into the characteristics that could be associated with a more effective intervention. Namely, successful trials tended to use emollients with an acidic pH of 5.5, applied over the entire body, and combined with topical corticosteroids in affected areas. Consensus on the optimal strategy to restore skin barrier function could help improve the homogeneity and clinical relevance of future trials on this topic. In the meantime, clinicians should avoid products associated with worse outcomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".