Biomarkers to predict changes in peanut allergy in children over time
Bibliographic record
Abstract
Background: Various biomarkers are used to define peanut allergy (PA). We aimed to observe changes in PA resolution and persistence over time comparing biomarkers in PA and peanut sensitised but tolerant (PS) children in a population-based cohort. Methods: Participants were recruited from the EAT and EAT-On studies, conducted across England and Wales and were generally well exclusively breastfed babies recruited at 3 months old and followed up until 11 years old. Clinical characteristics, skin prick test (SPT), sIgE to peanut and peanut components and mast cell activation tests (MAT) were assessed at 12m, 36m and 7-11y. Results: The prevalence of PA was 2.1% with only 1 child having PA resolution at 7-11y. PA children had larger SPT size, higher peanut-sIgE, Ara h 2-sIgE and MAT (all p<0.001) compared to PS children at 36m and 7-11y. SPT, peanut-sIgE, Ara h 2-sIgE and MAT between children with persistent PA, new PA, outgrown PA and PS were statistically significant at both 36m and 7-11y (p<0.001). Those with persistent PA had SPT, peanut-sIgE and Ara h 2-sIgE that increased over time and MAT which was highest at 36m. New PA children had increased SPT and peanut-sIgE from 36m to 7-11y, but MAT remained low. PS children had low biomarkers across time. Conclusions: In this cohort, few children outgrow or develop new PA between 36m and 7-11y. Children with PA have significantly higher SPT, peanut-sIgE, Ara h 2-sIgE and MAT compared to PS children, evident from 12-36m of age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".