Response to Biologics Along a Gradient of T2 Involvement in Patients With Severe Asthma: A Data-Driven Biomarker Clustering Approach
Bibliographic record
Abstract
BACKGROUND: Asthma with low levels of type 2 (T2) biomarkers is poorly understood. OBJECTIVE: To characterize severe asthma phenotypes and compare changes in asthma outcomes from pre- to postbiologic treatment along a gradient of T2 involvement. METHODS: This was a registry-based cohort study including data from 24 countries. Biomarker distribution (blood eosinophil count, fractional exhaled nitric oxide, and IgE) was quantified before biologic initiation. Clusters were identified using a 5-component Gaussian finite mixture model and phenotypically characterized. Changes in asthma and health care utilization outcomes between 1-year pre- and postbiologic initiation were compared between clusters and by biologic class. RESULTS: Among 3675 patients, 5 biomarker clusters were identified along a gradient of T2 involvement: cluster A with the lowest T2 involvement (16.4%), cluster B (20.4%), cluster C (22.9%), cluster D (30.3%), and cluster E with the highest T2 involvement (10.0%). In multivariable analysis, biologic use was associated with improved outcomes in all clusters but tended to be better at the higher end of the T2 spectrum. For example, patients in cluster C had a significantly greater increase in forced expiratory volume in 1 second compared with cluster A (difference 0.16 L [95% confidence interval: 0.08, 0.25]; P < .001). The odds of uncontrolled asthma were approximately 0.6 for all clusters compared with cluster A. Overall, exacerbation rates were lower, and greater improvements in lung function and asthma control were noted for anti-IL-5/5 receptor (R) (but not anti-IgE or anti-IL-4Rα) for all clusters compared with cluster A. CONCLUSION: T2-targeting biologics have utility in the management of asthma with low T2 involvement, but more effective therapies are needed. Further research is warranted to identify specific pathogenic pathways at the lower end of the T2 spectrum that can be effectively targeted by biologics.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".