The gender, age and risk factor distribution differs in self-reported allergic and non-allergic rhinitis: a cross-sectional population-based study
Bibliographic record
Abstract
BACKGROUND: Few population-based studies have assessed the prevalence and the risk factors of non-allergic rhinitis (NAR) in comparison to allergic rhinitis (AR). Moreover, epidemiologic data on rhinitis in the elderly subjects and in southern Europe are scarce. OBJECTIVE: This study aimed at estimating the prevalence and at comparing the risk factor distribution of AR and NAR in a general population sample aged 20-84 years in Italy. METHODS: A questionnaire on respiratory symptoms and risk factors was administered to random samples of the Italian population aged 20-44 (n = 10,494) 45-64 (n = 2167) and 65-84 (n = 1030) in the frame of the Gene Environment Interactions in Respiratory Diseases (GEIRD) study. Current AR and NAR were defined according to the self-reported presence of nasal allergies or of nasal symptoms without a cold or the flu. RESULTS: NAR showed a significant descending pattern in females from 12.0 % (95 % CI 11.1, 13.1) in the 20-44 year age class, to 7.5 % (5.4, 10.3) in the 65-84 year age class (p = 0.0009), and a roughly stable pattern in males, from 10.2 % (9.3, 11.2) to 11.1 % (8.4, 13.9) (p = 0.5261). AR decreased from 26.6 % (25.7, 27.6) in 20-44 years age class to 15.6 % (13.3, 18.0) in the 65-84 years age class (p < 0.0001), without gender difference. Subjects living near industrial plants and ex- and current smokers had a higher risk of NAR. Current smokers had a lower risk and subjects living in a Mediterranean climate a higher risk of AR. CONCLUSION: AR and NAR are fairly distinct conditions, as they have a different age, gender and risk factor distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".