Pollen exposure in pregnancy and infancy and risk of asthma hospitalisation - a register based cohort study
Bibliographic record
Abstract
BACKGROUND: A seasonal effect of month of birth and risk of allergic disease has been suggested by numerous studies. Few studies have directly measured pollen exposures at different points during pregnancy and in early life, and assessed their effects on risk of respiratory disease outcomes. METHODS: Pollen exposure was calculated for the first and last 12 weeks of pregnancy and the first 12 weeks of infancy for all children conceived by women residing in Stockholm, Sweden, between 1988 and 1995. Hospital admission data for respiratory conditions in the first year of life was also collected. RESULTS: Out of 110,381 children, 940 had been hospitalised for asthma by 12-months of age. Pollen levels showed both marked seasonal variations and between year differences. Exposure to high levels of pollen in the last 12 weeks of pregnancy was associated with an increased risk of asthma hospitalisation (aOR = 1.35, 95% CI = 1.07-1.71 for highest quartile versus remaining infants). Exposure to high levels of pollen in the first three months of life was associated with a reduced risk (aOR = 0.76, 95% CI = 0.59-0.98) but only in children of heavy smoking mothers. CONCLUSIONS: High levels of pollen exposure during late pregnancy were somewhat unexpectedly associated with an elevated risk of hospitalisation for asthma within the first year of life.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".