<scp>LEAP</scp>ing through the looking glass: secondary analysis of the effect of skin test size and age of introduction on peanut tolerance after early peanut introduction
Bibliographic record
Abstract
Abstract Background In the Learning Early About Peanut Allergy (LEAP) study, early peanut introduction in high‐risk 4‐ to 11‐month‐olds was associated with a significantly decreased risk of developing peanut allergy. However, the influences of key baseline high‐risk factors on peanut tolerance are poorly understood. Methods Secondary analysis was conducted on the publically available LEAP dataset, exploring relationships between peanut tolerance, baseline peanut/egg sensitization, eczema severity/duration, age of introduction, gender, and race. Results A multiple logistic regression model predicting odds of successful oral food challenge (OFC) at 60 months noted higher odds with early introduction (OR 9.2, P < 0.001, 95% CI 4.2–20.3), white race (OR 2.1, P = 0.04, 95% CI 1.1–3.9), and advancing age (OR 4.8, P = 0.04, 95% CI 1.1–20.8). Odds of peanut tolerance were lower with increasing peanut wheal size (OR 0.58, P < 0.001, 95% CI 0.46–0.74), increased baseline SCORAD score (OR 0.98, P = 0.04, 95% CI 0.97–1), and increased kUA/l of egg serum IgE (sIgE) (OR 0.99, P = 0.04, 95% CI 0.98–1). The probability of peanut tolerance in the early introduction group was 83% vs 43% in the avoidance group with SPT wheal of <4 mm. The probability of a successful OFC was significantly higher with peanut introduction between 6 and 11 months than at 4–6 months. Increasing eczema severity had limited impact on the probability of peanut tolerance in the early introduction arm. Conclusion Increasing peanut wheal size predicted peanut tolerance only in the avoidance arm. Peanut introduction between 6 and 11 months of age was associated with the highest rates of peanut tolerance, questioning the ‘urgency’ of introduction before 6 months.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".