Maternal food allergy is associated with daughters’ menarche in early adolescence
Bibliographic record
Abstract
RATIONALE: Associations between allergic disease and puberty amongst females have been widely studied. However, this association has received less attention in multigenerational populations. To this end, we sought to examine maternal allergic disease status ever, and daughters' menarche. METHODS: In a cohort of children born in 1995, in Manitoba, Canada, we considered maternal allergic disease ever to daughters' age 7-8 years, and daughters' menarche at ages 12-14 years. We included all participants for whom we had information on both the exposure and the outcome of those eligible. Data were analysed using descriptive statistics and logistic regression, with adjustment for confounding variables. RESULTS: Overall, the prevalences of maternal allergic diseases were 28.6% for asthma 18.8% for food allergy, 27.3% for eczema and 45.5% for rhinitis. By age 12-14 years, 41.6% (64/159) girls had reached menarche. Maternal food allergy was significantly associated with daughters' menarche (OR 4.39, 95% CI 1.51-12.73), whereas no association was found for maternal asthma, eczema or rhinitis. With consideration to comorbid disease, a combination of maternal asthma + food allergy was associated with daughters' menarche by age 12-14 years (OR 6.41; 95% CI 1.32-31.01). CONCLUSIONS: Maternal food allergy ever is associated with daughters' menarche by age 12-14 years.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".