Heterogeneity of asthma with nasal polyposis phenotypes: A cluster analysis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis with nasal polyposis (CRSwNP) affects a significant number of asthmatic patients and is notably associated with a more difficult-to-control asthma and marked inflammation. We need more studies on this specific asthma phenotype and its possible subphenotypes, in order to better individualize treatments. AIM: The aim of this study is to identify and characterize subphenotypes of asthma patients with CRSwNP using clinical, physiological and inflammatory variables. METHODS: K-means cluster analysis was performed on 17 clinical, physiological, and inflammatory variables from 1263 patients of all asthma severity and on a subpopulation of patients with asthma and CRSwNP. Study was registered on ClinicalTrials.gov (NCT03694847). RESULTS: On the overall population, three groups were identified. Cluster T1 (n = 708) are young, have a short asthma duration and a low prevalence of CRSwNP. Cluster T2 (n = 263) have the longest asthma duration and Cluster T3 (n = 292) are older with the shortest asthma duration. Patients in Clusters T2 and T3 have similar prevalences of CRSwNP. On the subpopulation of asthma with CRSwNP, three clusters were also identified. Cluster S1 (n = 83) have mild-to-moderate asthma with normal lung function. Clusters S2 (N = 53) and S3 (N = 42) include patients with severe asthma and decreased lung function, but those in Cluster S2 have a longer asthma duration, whereas those Cluster S3 have late-onset asthma. CONCLUSIONS: Despite coexistence of asthma and CRSwNP, not all patients have the same evolution of their asthma. Different phenotypes of asthma with CRSwNP can be identified and exploration of the characteristics of these subgroups could lead to a better individualized, targeted management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".