Peanut allergy: is maternal transmission of antigens during pregnancy and breastfeeding a risk factor?
Bibliographic record
Abstract
BACKGROUND: Peanut allergy is an important public health problem in western countries. However, the risk factors associated with this allergy remain uncertain. OBJECTIVE: To determine whether the consumption of peanuts during pregnancy and breastfeeding is a risk factor for peanut allergy in infants. METHODS: We enrolled 403 infants in a case-control study. The cases were infants aged 18 months or less with a diagnosis of peanut allergy based on a history of clinical reaction after exposure to peanuts and the presence of peanut-specific immunoglobulin E. Controls were age-matched infants with no known clinical history or signs of atopic disease. The mothers of the children filled out a detailed questionnaire about maternal diet during pregnancy and breastfeeding, the infant's diet, the presence of peanut products in the infant's environment, and family history of atopy. RESULTS: The mean (SD) age of cases was 1.23 (0.03) years. The groups were comparable in terms of the rate and duration of breastfeeding. However, the reported consumption of peanuts during pregnancy and breastfeeding was higher in the case group and associated with an increased risk of peanut allergy in offspring (odds ratio [OR], 4.22 [95% confidence interval [CI], 1.57-11.30 and OR, 2.28 [95% CI, 1.31-3.97] for pregnancy and breastfeeding, respectively). Overall, the infants with peanut allergy did not seem to be more exposed to peanut products in their environment than the controls. CONCLUSION: Early exposure to peanut allergens, whether in utero or through human breast milk, seems to increase the risk of developing peanut allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".