Blood IgE and Eosinophils are not Reliable Predictors of Nasal Tissue Eosinophils in Chronic Rhinosinusitis with Nasal Polyposis
Bibliographic record
Abstract
Abstract Introduction Chronic rhinosinusitis with nasal polyposis (CRSwNP) is a chronic inflammatory condition of the paranasal sinuses that is mainly associated with type-2 inflammation. Immunoglobulin E (IgE) and eosinophils in blood and nasal tissue have been suggested as biomarkers for the prognosis and severity of CRSwNP as well as indications for biological treatment. Objective The present study aims to assess the relationships between blood IgE concentration, blood eosinophil count, and nasal polyp eosinophil count in CRSwNP patients. Methods The present study is retrospective. Nasal polyps from CRSwNP patients (n = 73) were fixed and embedded in paraffin for hematoxylin and eosin stain. Blood was collected to measure IgE concentration and eosinophil count. Results Weak correlations were found between blood and tissue eosinophil counts (p = 0.004, r = 0.367) as well as blood IgE concentration and blood eosinophil count (p = 0.007, r = 0.372). There was no statistically significant correlation between blood IgE concentration and tissue eosinophil count. When dividing patients based on nasal polyp eosinophil count, blood eosinophil level was higher in the severely eosinophilic group than in the mildly eosinophilic group (p = 0.002). Conclusion Blood IgE and eosinophils are not reliable biomarkers to predict the inflammatory condition in CRSwNP. Further research is needed on the clinical roles of these biomarkers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".