Peanut sensitization in a group of allergic Egyptian children
Bibliographic record
Abstract
BACKGROUND: There are no published data on peanut sensitization in Egypt and the problem of peanut allergy seems underestimated. We sought to screen for peanut sensitization in a group of atopic Egyptian children in relation to their phenotypic manifestations. METHODS: We consecutively enrolled 100 allergic children; 2-10 years old (mean 6.5 yr). The study measurements included clinical evaluation for site of allergy, possible precipitating factors, consumption of peanuts (starting age and last consumption), duration of breast feeding, current treatment, and family history of allergy as well as skin prick testing with a commercial peanut extract, and serum peanut specific and total IgE estimation. Children who were found sensitized to peanuts were subjected to an open oral peanut challenge test taking all necessary precautions. RESULTS: Seven subjects (7%) were sensitized and three out of six of them had positive oral challenge denoting allergy to peanuts. The sensitization rates did not vary significantly with gender, age, family history of allergy, breast feeding duration, clinical form of allergy, serum total IgE, or absolute eosinophil count. All peanut sensitive subjects had skin with or without respiratory allergy. CONCLUSIONS: Peanut allergy does not seem to be rare in atopic children in Egypt. Skin prick and specific IgE testing are effective screening tools to determine candidates for peanut oral challenging. Wider scale multicenter population-based studies are needed to assess the prevalence of peanut allergy and its clinical correlates in our country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".