Cow’s milk allergy skin tests: fresh milk, commercial extracts, or both?
Bibliographic record
Abstract
BACKGROUND: The diagnosis of food allergy is based on a history of immediate allergic reaction following food ingestion, and skin prick test (SPT) demonstrating sensitization with commercial extracts (CE) or fresh food (FF). For most food allergens, the SPT with FF is considered more accurate and predictive. Regarding cow's milk, the results are inconclusive. This retrospective study aimed to evaluate the accuracy of SPT with fresh milk compared to CE (cow's milk and casein) for evaluation of cow's milk allergy (CMA). METHODS: This study summarized the medical records of children, diagnosed with CMA. The data include demographics, skin tests and oral food challenge results, as well as atopic comorbidities. RESULTS: Records of 698 patients with the diagnosis of CMA were reviewed, 388 fulfilled the inclusion criteria. Overall, 134 patients (34.54%) had an additional atopic disease. The SPT wheal size with fresh milk was significantly larger than with CE (cow's milk and casein) at first evaluation or before oral food challenge (OFC). Combination of SPT results (CE and FF) gave the maximal odds ratio for reaction during OFC and SPT with fresh milk alone gave the minimal OR (34.18 and 4.74, respectively). CONCLUSIONS: SPT with CE for CMA evaluation is more reliable than SPT performed with fresh milk. In patients suspected of having IgE-mediated CMA, before deciding on performing OFC, it is advised to perform SPT with at least two different extracts, and always include casein. Fresh milk can serve as a backup if commercial extracts are not available. In cases that the SPT with fresh milk is 3 mm or less, there is 93.3% chance that the OFC will pass without reaction. Trial registration This study protocol was reviewed and approved by the Ethics Committee of Meir Medical Center, IRB Number 0083-18 MMC.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".