Sensitisation patterns and allergy outcomes in pregnant women living in the urban area
Bibliographic record
Abstract
BACKGROUND: Worldwide, allergy affects more than one billion people, with particularly rising prevalence in industrialised areas. Specifically, young adults appear to be predominantly targeted for an allergy diagnosis. Allergic diseases in pregnancy are mainly pre-existing but could also occur de novo. The immunological changes while pregnant, with increased Th2 lymphocyte activity, can facilitate allergen sensitisation. OBJECTIVE: The aim of this study was to evaluate the pattern of specific IgE (sIgE) sensitisation to common inhalant and food allergens in pregnancy, and assess its relationship to self-reported allergic disease. METHODS: We assessed 200 pregnant women, aged 20-38 years (mean age = 29 years), participant of ELMA (Epigenetic Hallmark of Maternal Atopy and Diet) study, living in a metropolitan area, with no pregnancy associated metabolic complications, for total IgE and allergen specific IgE to 20 allergens. RESULTS: 48% of pregnant women were sensitised to at least one allergen, at a cut-off point of 0.35 kU/L and they were assigned as atopic. However 42% in atopic group were not reporting any allergic disease. The most common inhalant allergens were: pollen (24.5%) and animal dander (23.5%). The most common food allergens were: cow's milk (5.5%) and apples (4.5%). 7.5% of women reported asthma, 21.5% allergic rhinitis, 11.5% atopic dermatitis and 18.5% food allergy. 8.5% of were taking medication for asthma or allergies. Atopic dermatitis had the highest tendency to become more severe during pregnancy. Total IgE values were significantly higher in atopic women. CONCLUSIONS: Allergic sensitisation is a common phenomenon in pregnancy. Some sensitisations could be asymptomatic. Further studies should investigate if sensitisation in mothers confers risks for immune alterations in their children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".