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
Children with milk and/or egg allergy can often tolerate heated forms of these foods. Skin prick testing (SPT) with commercial extracts followed by a possible oral food challenge (OFC) are routinely performed in these children. This study evaluated the clinical utility of a negative SPT with the real extensively heated milk or egg in predicting whether a child would tolerate an OFC to the heated food. Charts were reviewed in a single allergy clinic for any patient with a negative skin SPT to heated milk or egg, prepared in the form of a muffin. Data was collected on the success of the OFC to the muffin as well as age, sex, symptoms and co-morbidities in these patients. Fifty-eight patients had negative SPT to the heated milk or egg in a muffin. All of these children underwent OFC to the appropriate heated food in the outpatient clinic. Fifty-five of these patients tolerated the OFC. The negative predictive value for the SPT with the extensively heated food product was 94.8%. SPT with heated 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 | 0.001 |
| 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".