The association between allergy and sinusitis: a cross-sectional study based on NHANES 2005–2006
Bibliographic record
Abstract
BACKGROUND: The relationship between allergies and sinusitis, though extensively studied, remains poorly defined. While several studies proposed a cause-and-effect relationship between allergy and chronic sinusitis, several others reported the lack of any existing association. This study aimed to investigate the relationship between allergy and sinusitis. METHODS: We conducted a cross-sectional study using a representative sample of the US population from the National Health and Nutrition Examination Survey 2005‒2006 (n = 7244). A self-reported allergy questionnaire and total and allergen-specific IgE levels were used for analysis. Participants were divided into positive and negative allergy symptoms groups (PAS, NAS, respectively) to eliminate the influence of allergy symptoms on the apparent incidence of sinusitis. Pearson's chi-square test and the linear regression analysis using Durbin Watson test were used for statistical analysis. RESULTS: Sinusitis incidence in the PAS group (22.4%; 521/2327) was significantly higher than that in the NAS group (7.1%; 348/4917) [odds ratios (OR) = 3.788, 95% confidence interval (CI) 3.272‒4.384, P < 0.001]. sinusitis incidence in non-sensitized and sensitized groups was not statistically different. After controlling for allergy symptoms, there was a negative correlation between sensitization status and the occurrence of sinusitis in the PAS group (OR = 1.407, 95% CI 1.156‒1.711, P < 0.01). Increase in serum total IgE levels correlated with decrease in incidence of sinusitis in both PAS and NAS groups. sinusitis incidence was significantly reduced in the PAS group in participants sensitized to allergens such as cockroaches, ragweed, ryegrass, Bermuda grass, oak, birch, and thistle. CONCLUSION: Allergy is related to sinusitis incidence. It is likely that sensitization status could reduce the incidence of sinusitis, albeit in an antigen-specific manner.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".