Diagnosis of Peanut Allergy in Preschool Children: The Impact of Skin Testing With a Novel Composition of Peanuts
Bibliographic record
Abstract
Peanut allergy is an increasing concern in younger children. Available bedside diagnostic tools, i.e., prick tests with commercial extracts or peanut-containing foods have only limited predictive values. In a cohort of preschoolers with both a history of allergic reactions and sensitization to peanut proteins, we aimed to characterize the impact of skin tests with a novel composition of peanuts LPP-MH. Almost one quarter (27/110) of preschool children, with a history of allergic reactions to peanuts and positive standard IgE-mediated tests for peanut allergy, can tolerate the reintroduction of peanut proteins into their diet after resolving their allergy and, thus, can avoid adverse health outcomes associated with the false diagnosis. In the younger age group, a quarter of peanut allergic children, display a relatively high threshold, potentially enabling an easier and safer oral immunotherapy protocol in this window of opportunity in childhood. The use of the novel diagnostic skin test, LPP-MH, significantly improves the predictive value of outpatient evaluation for the outcomes of peanut challenge as well as the expected threshold at which the PA child will react, thus, making for a better informed decision of how, when, and where to challenge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".