Association between preterm birth and maternal allergy considering IgE level
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the association between maternal allergies and preterm birth by different total immunoglobulin E (IgE) levels. METHODS: Data of 81 791 pregnant women from the Japan Environment and Children's Study, a prospective birth cohort, were used. Maternal allergic diseases, including a history of bronchial asthma (BA), atopic dermatitis (AD), and allergic rhinitis (AR), were obtained by self-administered questionnaires. Total serum IgE levels were measured at the first trimester and obstetrical outcomes from medical records transcripts were analyzed. The association between maternal allergic disease and obstetric outcome, including threatened abortion, preterm labor, early preterm birth (22-33 weeks), and late preterm birth (34-36 weeks), were examined by logistic regression. Subgroup analyses were performed by IgE level. RESULTS: Maternal BA and AR were associated with an increased risk of threatened abortion and preterm labor, but high total IgE level was associated with a decreased risk of preterm labor. There was little difference in associations between allergic disease and threatened abortion and preterm labor by total IgE levels. Although there was no significant association between allergic disease and preterm birth, if total IgE was high, AR was significantly associated with a decreased risk of early preterm birth (adjusted odds ratio, 0.60; 95% confidence interval 0.43-0.86). There was significant evidence for differences associated with total IgE levels (P-values for the interaction of the effects of AD and AR on early preterm birth were 0.039 and 0.015, respectively). CONCLUSIONS: The effect of allergy on preterm birth might differ depending on the total IgE level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".