Active smoking status in chronic rhinosinusitis is associated with higher serum markers of inflammation and lower serum eosinophilia
Bibliographic record
Abstract
BACKGROUND: Smoking negatively affects postoperative evolution in patients with chronic rhinosinusitis (CRS); however, the mechanism remains incompletely described. In the lung, smoking increases expression of proinflammatory genes and is associated with an elevation of inflammatory serum markers. Our objective is to determine the impact of smoking on these biomarkers in CRS. METHODS: Two existing populations of patients previously phenotyped for genetic association studies (206 patients with refractory CRS and 408 patients with CRS and nasal polyposis) were stratified according to self-reported smoking status and available serum biomarkers (complete blood count [CBC], total immunoglobulin E [IgE]). Asthma and bacterial cultures were evaluated. RESULTS: Active smoking was low in both groups (genetics of chronic rhinosinusitis 1 [GCRS1]: 11.2%; genetics of chronic rhinosinusitis 2 [GCRS2]: 9.4%). Total white blood cell (WBC) count was significantly higher in active smokers than in those who had never smoked and ex-smokers. Serum eosinophilia and prevalence of self-reported asthma was lower in active smokers than never-smokers. In the GCRS2 population, endoscopically-collected cultures trended toward a lower recovery rate of Staphylococcus aureus in smokers (p = 0.07). Never-smokers and ex-smokers had similar levels of WBCs and eosinophils. CONCLUSION: Our study reveals that active tobacco smoking is associated with increases in markers of systemic inflammation in patients with CRS. The proinflammatory effect of smoking seems not only to act locally on sinus mucosa as previously described, but may also influence levels of inflammatory biomarkers systemically, suggesting that smoking-induced changes have profound implications for health. Nevertheless, these changes may be potentially reversible; thus smoking cessation in CRS patients is strongly advised, and may have an impact on response of CRS to therapy.
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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) | 0.002 | 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".