Identifying super-responders
Bibliographic record
Abstract
Asthma is a chronic respiratory disease marked by heterogeneity and variable clinical outcomes. Recent therapeutic advances have highlighted patients achieving optimal outcomes, termed "remission" or "super-response." This review evaluates the various definitions of these terms and explores how disease burden impedes the attainment of remission. We assessed multiple studies, including a recent systematic review and meta-analysis, on biologic treatments for asthma remission. Our review highlights that type 2 inflammation may be the strongest predictor of biologic response. Key comorbidities (eg, obesity and mood disorders) and behavioral factors (eg, poor adherence, improper inhalation technique, and smoking) were identified as dominant traits limiting remission. In addition, asthma burden and longer disease duration significantly restrict the potential for remission in patients with severe asthma under the current treatment paradigm. We review the potential for a "predict-and-prevent" approach, which focuses on early identification of high-risk patients with type 2 inflammation and aggressive treatment to improve long-term asthma outcomes. In conclusion, this scoping review highlights the following unmet needs in asthma remission: (1) a harmonized global definition, with better defined lung function parameters; (2) integration of nonbiologic therapies into remission strategies; and (3) a clinical trial of early biologic intervention in patients with remission-prone, very type 2-high, moderately severe asthma with clinical remission as a predefined primary end point.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".