Rhinosinusitis symptoms, smoking and <scp>COPD</scp>: Prevalence and associations
Bibliographic record
Abstract
OBJECTIVE (S): To estimate the prevalence and associations among rhinosinusitis symptoms, smoking and chronic obstructive pulmonary disease (COPD). DESIGN: Cross-sectional study. SETTING: Population-based. PARTICIPANTS: All adults aged 40 years or more living in the selected households in the city of Florianópolis (Florianópolis, Santa Catarina, Brazil). MAIN OUTCOME MEASURES: Assessment instruments comprised household interviews, anthropometric measurements and spirometry. Rhinosinusitis symptoms were based on the responses to the 22-item Sinonasal Outcome Test (SNOT-22) questionnaire; smoking status was defined by the criteria of the CDC, and the functional diagnosis of COPD was done by spirometry. RESULTS: The prevalence (n = 1056) of rhinosinusitis symptoms, smoking and COPD was 14.7%, 17.9% and 8.7%, respectively. Multivariate analysis showed that, with the exception of COPD, all other clinical variables (smoking, previous diagnosis of rhinitis, previous diagnosis of gastritis/ulcer/gastroesophageal reflux, and symptoms of depression) remained associated with higher prevalence of rhinosinusitis symptoms. CONCLUSIONS: Rhinosinusitis symptoms were common both in smokers and in patients with COPD. However, only tobacco was significantly associated with rhinosinusitis symptoms and can act as a cofounder in the association between COPD and rhinosinusitis symptoms.
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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 | 0.000 | 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".