Assessing the relationship between maternal peanut consumption during pregnancy and the prevalence of peanut allergy in a birth cohort
Bibliographic record
Abstract
Allergy is one of the most common chronic diseases of childhood and often presents early in life. Food allergies are typically the initial presentation of allergic disease. In the past, many medical/pediatric societies recommended avoidance or delayed introduction of certain foods, especially peanuts, in an attempt to reduce the risk of food allergy in children. However, it is now realized that this may not be the correct advice to give all mothers. The Canadian Healthy Infant Longitudinal Development (CHILD) study is a national, general population-based, longitudinal birth cohort study across four different centres in Canada: Vancouver, Edmonton, Winnipeg and Toronto. Data including maternal food frequency questionnaires and results of skin prick testing (SPT) to foods were collected. This data from the CHILD study was analyzed using a statistical analysis system (SAS) in order to determine the relationship between maternal consumption of peanut during pregnancy and the outcome of peanut sensitization as defined by SPT. Two mean wheal diameter cut-off points at 2 mm or greater or 3 mm or greater, which are commonly used in epidemiological studies, were used to indicate sensitization in this study. An unadjusted association was only found between maternal consumption of peanuts, other nuts and seeds and sensitization to peanut as indicated by development of a wheal measuring 3 mm or greater in diameter in response to skin prick testing.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".