The health and economic outcomes of early egg introduction strategies
Bibliographic record
Abstract
BACKGROUND: Studies suggest early egg introduction (EEI) in the first year of life is associated with reduced risk of developing egg allergy. No US recommendations exist regarding optimally implementing EEI. METHODS: Using simulation and Markov modelling over a 20-year horizon, we explored optimal EEI strategies applied to US, European and Canadian populations, comparing screening of high-risk infants (skin prick testing [SPT] or serum-specific IgE[sIgE]) before introducing cooked egg at 6 months of life vs egg introduction at home, without screening, for all infants. RESULTS: A no-screen approach dominated egg SPT screening of high-risk infants with early-onset eczema. Base model per-patient incremental costs of SPT were $6865 US dollars (USD), 6801 euros and $10 610 Canadian dollars (CAD). For egg sIgE screening in primary care settings, base model incremental costs were $16 722 USD, 18 072 euros and $28 193 CAD. As the simulation concluded 2.5% were egg allergic without screening vs 9.5%, 12% and 21.4% of children undergoing SPT, delayed introduction or sIgE screening. Incremental societal costs from screening reached $2 009 351 175 USD for SPT and $4 894 445 790 USD for sIgE testing. In sensitivity analyses, if the risk of reaction with initial egg ingestion was ≥22.5%, SPT before EEI became a preferred strategy. A no-screen approach dominated both EEI of raw pasteurized egg and delayed cooked egg introduction approaches. CONCLUSIONS: Assuming initial reaction rates < 22.5%, a no-screening EEI cooked egg approach has superior health and economic benefits in terms of number of egg allergy cases prevented and total healthcare costs vs screening testing.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".