BARRIERS TO IMPLEMENTATION OF EARLY PEANUT INTRODUCTION AMONG PEDIATRICIANS, FAMILY PHYSICIANS, AND ALLERGISTS
Bibliographic record
Abstract
Abstract BACKGROUND The approach to peanut allergy prevention has shifted with publication of the Learning Early About Peanut (LEAP) trial and recently released NIAID guideline recommending early peanut introduction in high risk infants. OBJECTIVES Our objective was to determine whether current practice patterns at both the allergist and primary care level are in keeping with the LEAP recommendations and NIAID guideline. DESIGN/METHODS A 17-question survey was distributed in 2016 to Canadian allergists through the Canadian Society of Allergy and Clinical Immunology, paediatricians through the Canadian Paediatric Society, and a sample of practicing family physicians. RESULTS There was variability in the definition of infants at high risk for peanut allergy and recommendations for age of introduction of allergenic solids. There was also variability in how often allergist evaluation was recommended for infants with egg allergy or severe eczema prior to peanut introduction, with allergists 9 times more likely to recommend pre-emptive peanut testing in infants with severe eczema prior to peanut introduction. The majority of family physicians (77.1%), paediatricians (91.4%) and allergists (89.1%) did not believe there was harm to introduction of solids between 4–6 months of age, or that breastfeeding rates would be affected with earlier solid introduction. CONCLUSION Further education about the implications of LEAP is required. There are broad issues (such as the definition of a high risk infant) that require international consensus. Most primary care physicians and allergists do not believe there are harms to introduction of allergenic solids prior to 6 months of age, or that breastfeeding rates will be affected.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".