Antenatal risk factors for peanut allergy in children
Bibliographic record
Abstract
BACKGROUND: Prenatal factors may contribute to the development of peanut allergy. We evaluated the risk of childhood peanut allergy in association with pregnancy exposure to Rh immune globulin, folic acid and ingestion of peanut-containing foods. METHODS: We conducted a web-based case-control survey using the Anaphylaxis Canada Registry, a pre-existing database of persons with a history of anaphylaxis. A total of 1300 case children with reported peanut allergy were compared to 113 control children with shellfish allergy. All were evaluated for maternal exposure in pregnancy to Rh immune globulin and folic acid tablet supplements, as well as maternal avoidance of dietary peanut intake in pregnancy. RESULTS: Receipt of Rh immune globulin in pregnancy was not associated with a higher risk of peanut allergy (odds ratio [OR] 0.86, 95% confidence interval [CI] 0.51 to 1.45), nor was initiation of folic acid tablet supplements before or after conception (OR 0.53, 95% CI 0.19 to 1.48). Complete avoidance of peanut-containing products in pregnancy was associated with a non-significantly lower risk of peanut allergy (OR 0.53, 95% CI 0.27 to 1.03). CONCLUSION: The risk of childhood peanut allergy was not modified by the following common maternal exposures in pregnancy: Rh immune globulin, folic acid or peanut-containing foods. CLINICAL IMPLICATIONS: Rh immune globulin, folic acid supplement use and peanut avoidance in pregnancy have yet to be proven to modulate the risk of childhood anaphylaxis to peanuts. CAPSULE SUMMARY: Identification of prenatal factors that contribute to peanut allergy might allow for prevention of this life-threatening condition. This article explores the role of three such factors.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".