Descriptive analysis of oral food challenge outcomes at a tertiary care center
Bibliographic record
Abstract
Oral food challenges are the gold standard for clinical tolerance. Predictors of failing a challenge are needed for clinicians. A retrospective chart review of 2010 food challenges was performed. Descriptive analysis follows. We assessed 113 challenges (35 peanut, 28 egg, 12 tree nut, 11 milk, 27 other). There were 29 failures (22 objective, 7 subjective). Among objective challenge failures, 4/7 cashew (57%), 10/35 (29%) peanut, and 6/28 egg (21%) failed. There were no failed milk challenges. Most (79%) failed peanut/cashew challenges occurred at doses ≤1.0g while 50% of failed egg challenges were final dose (10g). Three children required epinephrine (all cashew), none of whom had a prior known exposure (skin tested 2° peanut/almond). For peanut failures, 40% were history negative. The remainder of the challenge failure reactions were similar to the presenting reaction. Factors for failed challenges compared with successful challenges included atopic dermatitis (100% v 75%), asthma (93% v 63%), and other food allergy (64% v 48%). The majority of challenge failures were to peanut while the most severe reactions were to cashew, and occurred in patients without prior known exposure. Failures to peanut and cashew occurred at low doses while most egg reactions occurred at high doses. Those who failed a challenge had more atopic disease than those who passed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".