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 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 | 0.001 |
| 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".