Change in prevalence of IgE sensitization over 20 years in the European community respiratory health survey cohort
Bibliographic record
Abstract
Background: Cross-sectional studies show a lower prevalence of IgE sensitisation in older adults, but few population-based cohort studies have examined whether this is an aging or a cohort effect. Aims and objectives: To assess changes in IgE sensitisation in European adults as they aged over 20 years. Methods: Within the European Community Respiratory Health Survey, serum specific IgE to common aeroallergens (house dust mite, cat, grass) was measured in 3206 adults (age at baseline: 22-44 years), from 25 centres, on three occasions over 20 years. Changes in sensitisation were analysed by regression analysis with inverse sampling-probability weights, with Huber variances and participants as clusters. These analyses were corrected for potential differences in laboratory equipment. Results: Overall, the prevalence of sensitisation to at least one of the three measured allergens fell from 29.4% to 24.8% (-4.6%, 95%CI: -7.0% to -2.1%). Sensitisation to house dust mite (-4.3%, 95%CI: -6.0% to -2.6%) and cat (-2.1%, 95%CI: -3.6% to -0.7%) fell significantly, but no significant change was seen for sensitisation to grass (-0.6%, 95%CI: -2.5% to 1.3%). Age-specific prevalence of sensitisation to house dust mite and cat did not differ between birth cohorts, but sensitisation to grass was most prevalent in the most recent birth cohorts. Conclusion: While there was evidence that aging was associated with lower levels of sensitisation to house dust mite and cat, this was not seen for sensitisation to grass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".