Prevalence of allergic and nonallergic rhinitis in a rural area of northern China based on sensitization to specific aeroallergens
Bibliographic record
Abstract
Most epidemiologic studies reporting prevalence of allergic rhinitis (AR) and nonallergic rhinitis (NAR) have assessed solely self-reported prevalence, without confirmation by objective measures. Furthermore, reports of prevalence of NAR in Chinese subjects are scarce. Thus, we aimed to explore the prevalence and risk factors of AR and NAR in a Chinese, based on both clinical manifestation and allergic status. We conducted a population-based cross-sectional survey, involving 1084 local residents from a rural area of Beijing, China. Participants were enrolled using a stratified two-stage cluster sampling method. All adult participants or the guardians of children completed standardized questionnaires to provide relevant demographic and clinical information. Skin prick tests were also performed to determine sensitization to specific aeroallergens. AR/NAR was classified according to Allergic Rhinitis and its Impact on Asthma criteria. Prevalence of self-reported AR was 46.80%. Based on SPT results, the confirmed standardized prevalence of AR and NAR were 16.78% and 24.60%, respectively. Severity scores for nasal itching, sneezing, rhinorrhea and congestion were significantly higher in subjects with AR, than subjects with NAR ( P < 0.05 for all). The three most common aeroallergens in self-reported AR group were Blattella germanica (16.6%), Dermatophagoides farinae (14.6%), and Dermatophagoides pteronyssinus (13.9%). Family history of AR and atopic dermatitis were significantly associated with AR (adjusted OR: 4.97 and 2.69, respectively), whereas family history of AR and asthma were significantly associated with NAR (adjusted OR: 3.53 and 2.45, respectively). Similarly, comorbid asthma, CRS, and atopic dermatitis were significant risk factors for both AR and NAR. Combination of standardized questionnaires and specific allergen tests may provide more accurate estimates of prevalence of AR and NAR and associated risk factors.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".