Food allergy prevention through the decades: An ounce of humility is worth a pound of cure
Bibliographic record
Abstract
Food allergy prevention has undergone a significant transformation over the past 3 decades. This review provides an overview of the evolution of food allergy prevention, highlighting changes in guidance, cost-effectiveness of prevention, the role of shared decision-making, and the emergence of oral immunotherapy for those in whom primary prevention fails. Changes to food allergy prevention over recent decades can be conceptualized into five epochs, which have followed a general trend of loosening restrictions on the allergen introduction timeline. These epochs are characterized by significant maternal and infant dietary restrictions in the "universal avoidance epoch"(-1990), loosened maternal diet restrictions in the "infant avoidance epoch" (1990-2000), a time-bound allergen introduction schedule in the "stratified avoidance epoch" (2000-2010), retraction of recommendations in the "corrective retraction epoch" (2010-2015), and endorsement of early allergen introduction in the "early introduction epoch" (2015-present), the start of which is marked by the 2015 Learning Early About Peanut study. In hindsight, it is clear that certain recommendations from previous decades were not the best course of action. A no-screening early introduction approach to food allergy prevention is both cost-effective and beneficial to patient quality of life.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".