“To screen or not to screen”: Comparing the health and economic benefits of early peanut introduction strategies in five countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Early peanut introduction (EPI) in the first year of life is associated with reduced risk of developing peanut allergy in children with either severe eczema and/or egg allergy. However, EPI recommendations differ among countries with formal guidelines. METHODS: Using simulation and Markov modeling over a 20-year horizon to attempt to explore optimal EPI strategies applied to the US population, we compared high-risk infant-specific IgE peanut screening (US/Canadian) with the Australiasian Society for Clinical Immunology and Allergy (Australia/New Zealand) (ASCIA) and the United Kingdom Department of Health (UKDOH)-published EPI approaches. RESULTS: Screening peanut skin testing of all children with early-onset eczema and/or egg allergy before in-office peanut introduction was dominated by a no screening approach, in terms of number of cases of peanut allergy prevented, quality-adjusted life years (QALY), and healthcare costs, although screening resulted in a slightly lower rate of allergic reactions to peanut per patient in high-risk children. Considering costs of peanut allergy in high-risk children, the per-patient cost of early introduction without screening over the model horizon was $6556.69 (95%CI, $6512.76-$6600.62), compared with a cost of $7576.32 (95%CI, $7531.38-$7621.26) for skin test screening prior to introduction. From a US societal perspective, screening prior to introduction cost $654 115 322 and resulted in 3208 additional peanut allergy diagnoses. Both screening and nonscreening approaches dominated deliberately delayed peanut introduction. CONCLUSIONS: A no-screening approach for EPI has superior health and economic benefits in terms of number of peanut allergy cases prevented, QALY, and total healthcare costs compared to screening and in-office peanut introduction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it