Inflammatory Profile of Chronic Rhinosinusitis With Nasal Polyp Patients in Brazil: Multicenter Study
Bibliographic record
Abstract
OBJECTIVES: To determine the inflammatory profile of CRSwNP in Brazil and characterize the subgroups of CRSwNP patients in this population through cluster analysis. STUDY DESIGN: Multicenter cross-sectional study involving 15 centers representing different regions of Brazil. SUBJECTS AND METHODS: Clinical data of 166 patients and 80 controls, aged 18 to 70 years old, number of surgeries for CRS, history of asthma and aspirin sensitivity, and Lund-Mackay scores on CT scans. During nasal endoscopy, we obtained the Lund-Kennedy scores and collected 2 samples of nasal polyps: one for eosinophil and neutrophil tissue counts and one to quantify different cytokines. RESULTS: 79.6% of our patients had 10 or more eosinophils/HPF. CRSwNP groups exhibited significantly lower concentrations of TNF-alpha and significantly higher concentrations of IFN-gamma, CCL11/Eotaxin, CCL24/Eotaxin-2/MPIF-2, and CCL26/Eotaxin-3 versus the control group (Kruskal-Wallis test). Comparison between CRSwNP groups (≥10 vs <10 eosinophils/HPF) showed no difference in cytokine concentration (Mann-Whitney test). Hierarchical clustering and PCA according to cytokine concentrations revealed 2 main Clusters, with a significantly higher concentration of all cytokines in Cluster 1 (n = 35) than in Cluster 2 (n = 121), except IL-6 and IL-33 (Mann-Whitney test). According to ROC curve analysis the best cut-off to differentiate the 2 clusters was 43 eosinophils/HPF. The group with ≥43 presented a higher prevalence of men and a higher Lund-Mackay score (Mann-Whitney test). CONCLUSIONS: CRSwNP patients in Brazil present mixed inflammation, with 2 distinct groups (high and low inflammatory pattern) that can be distinguished by tissue eosinophilia of ≥43 eosinophils/HPF cut-off in nasal polyps.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".