Skin prick testing with extensively heated milk or egg products helps predict the outcome of an oral food challenge: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Cow's milk and hen's egg are the most frequently encountered food allergens in the pediatric population. Skin prick testing (SPT) with commercial extracts followed by an oral food challenge (OFC) are routinely performed in the diagnostic investigation of these children. Recent evidence suggests that milk-allergic and/or egg-allergic individuals can often tolerate extensively heated (EH) forms of these foods. This study evaluated the predictive value of a negative SPT with EH milk or egg in determining whether a child would tolerate an OFC to the EH food product. METHODS: Charts from a single allergy clinic were reviewed for any patient with a negative SPT to EH milk or egg, prepared in the form of a muffin. Data collected included age, sex, symptoms of food allergy, co-morbidities and the success of the OFC to the muffin. RESULTS: Fifty-eight patients had negative SPTs to the EH milk or egg in a muffin and underwent OFC to the appropriate EH food in the outpatient clinic. Fifty-five of these patients tolerated the OFC. The negative predictive value for the SPT with the EH food product was 94.8%. CONCLUSIONS: SPT with EH milk or egg products was predictive of a successful OFC to the same food. Larger prospective studies are required to substantiate these findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".