Establishing the diagnosis of peanut allergy in children never exposed to peanut or with an uncertain history: a cross-Canada study
Bibliographic record
Abstract
The diagnosis of peanut allergy (PA) can be complex especially in children never exposed to peanut or with an uncertain history. The aim of the study is to determine which diagnostic algorithms are used by Canadian allergists in such children. Children 1-17 yrs old never exposed to peanut or with an uncertain history having an allergist-confirmed diagnosis of PA were recruited from the Montreal Children's Hospital (MCH) and allergy advocacy organizations. Data on their clinical history and confirmatory testing were compared to six diagnostic algorithms: I. Skin prick test (SPT) >or=8 mm or specific IgE >or=5 kU/l or positive food challenge (+FC); II. SPT >or=8 or IgE >or=15 or +FC; III. SPT >or=13 or IgE >or=5 or +FC; IV. SPT >or=13 or IgE >or=15 or +FC; V. SPT >or=3 and IgE >or=5 or IgE >or=5 or +FC; VI. SPT >or=3 and IgE >or=15 or IgE >or=15 or +FC. Multivariate logistic regression analysis was used to identify factors associated with the use of each algorithm. Of 497 children recruited, 70% provided full data. The least stringent algorithm, algorithm I, was applied in 81.6% (95% CI, 77-85.6%) of children and the most stringent, algorithm VI, in 42.6% (95% CI, 37.2-48.1%).The factor most associated with the use of all algorithms was diagnosis made at the MCH in those never exposed to peanut. Other factors associated with the use of specific diagnostic algorithms were higher paternal education, longer disease duration, and the presence of hives, asthma, eczema, or other food allergies. Over 18% (95% CI, 14.4-23.0%) of children were diagnosed with PA without fulfilling even the least stringent diagnostic criteria.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".