Predicting Cow’s Milk Challenge Outcomes in Children: Multivariate Analysis of Clinical Predictors
Bibliographic record
Abstract
Introduction: Oral food challenges are the gold standard for diagnosis and reactivity thresholds but are resource intensive and high risk for reactions. Limited data on factors associated with increased risk of positive oral food challenges exist. We aimed to assess factors associated with positive oral food challenges and create a model to predict cow milk oral food challenge outcomes. METHODS: Children aged 5-18 being considered for cow's milk oral immunotherapy underwent a single-blind, placebo-controlled food challenge to cow's milk, with either positive (reaction) or negative (tolerance) outcomes. Initial factors recorded included sex, age, history of asthma, eczema, allergic rhinitis, prior epinephrine use for cow's milk-induced reactions, skin prick test size, serum levels of immunoglobulin E antibodies to α-lactalbumin, β-lactoglobulin, and casein, and log-transformed values. Stepwise backward multivariate Firth bias-reduced logistic regression was used to create the final model, and performance was assessed with receiver operator characteristic curves. RESULTS: A total of 111 children underwent an oral food challenge, 103 patients reacted, and 8 tolerated the challenge. Univariate analysis showed skin prick test size, previous epinephrine use, history of asthma, and log-transformed α-lactalbumin, β-lactoglobulin, and casein were significantly associated with positive oral food challenge. The multivariate model included two factors: log-transformed casein (aOR 2.4; 95% CI: 1.4-5.9; p < 0.001) and previous epinephrine use (aOR 6.5; 95% CI: 1.2-68.0; p = 0.03). The final model showed good discriminatory performance (AUC 0.928; 95% CI: 0.83-0.98). In comparison, a univariate model using only the skin prick test (OR 1.44, 95% CI: 1.1-2.0; p = 0.002) had worse discriminatory performance (AUC 0.83; 95% CI: 0.64-0.93). CONCLUSION: The study suggests that logistic multivariate models, including log-transformed casein and previous epinephrine use, may help predict oral food challenge outcomes in pediatric patients. Future studies are needed to validate this with larger datasets. .
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".