Continuous Rather Than Solely Early Farm Exposure Protects From Hay Fever Development
Bibliographic record
Abstract
BACKGROUND: An important window of opportunity for early-life exposures has been proposed for the development of atopic eczema and asthma. OBJECTIVE: However, it is unknown whether hay fever with a peak incidence around late school age to adolescence is similarly determined very early in life. METHODS: In the Protection against Allergy-Study in Rural Environments (PASTURE) birth cohort potentially relevant exposures such as farm milk consumption and exposure to animal sheds were assessed at multiple time points from infancy to age 10.5 years and classified by repeated measure latent class analyses (n = 769). Fecal samples at ages 2 and 12 months were sequenced by 16S rRNA. Hay fever was defined by parent-reported symptoms and/or physician's diagnosis of hay fever in the last 12 months using questionnaires at 10.5 years. RESULTS: Farm children had half the risk of hay fever at 10.5 years (adjusted odds ratio [aOR] 0.50; 95% CI 0.31-0.79) than that of nonfarm children. Whereas early life events such as gut microbiome richness at 12 months (aOR 0.66; 95% CI 0.46-0.96) and exposure to animal sheds in the first 3 years of life (aOR 0.26; 95% CI 0.06-1.15) were determinants of hay fever, the continuous consumption of farm milk from infancy up to school age was necessary to exert the protective effect (aOR 0.35; 95% CI 0.17-0.72). CONCLUSIONS: While early life events determine the risk of subsequent hay fever, continuous exposure is necessary to achieve protection. These findings argue against the notion that only early life exposures set long-lasting trajectories.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".