Molecular Endotypes (Type 1, 2, and 3) and Treatment Response in Chronic Rhinosinusitis: A Systematic Review of the Literature
Bibliographic record
Abstract
ABSTRACT Introduction: Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disorder affecting the nasal and paranasal sinus mucosa for more than 12 weeks, with a significant global health impact. The conventional phenotypic classification into CRS with and without nasal polyps is insufficient to capture its immunological complexity. Recent research supports a molecular endotyping approach, each associated with distinct immunological markers and treatment responses. Objective: To systematically evaluate the relationship between molecular endotypes of CRS and their respective responses to medical and surgical treatment. Methods: A systematic review of the literature was conducted, and 10 studies were included in the qualitative synthesis. These studies were assessed for risk of bias (RoB) using the RoB 2 tool for randomized trials and the Newcastle–Ottawa Scale for observational studies. Results: Type 2 inflammation – characterized by elevated Interleukin (IL)-4, IL-5, IL-13, and eosinophilia – showed a superior response to corticosteroids and biologics such as dupilumab and mepolizumab. In contrast, Type 1 and Type 3 endotypes (linked to Th1/Th17 cytokines and neutrophilia) responded better to antibiotics and macrolides but poorly to corticosteroids and anti-Th2 therapies. The most significant numerical outcome was a 76% reduction in the need for systemic corticosteroids or surgery in Type 2 CRS patients treated with dupilumab. Discussion: The review confirms that treatment efficacy in CRS is strongly influenced by underlying molecular endotypes. Type 2 inflammation is a robust predictor of good response to corticosteroids and biologics. Conversely, Types 1 and 3 require alternative treatments, and future research should focus on targeted therapies for these less responsive endotypes.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".