Impact of severe exacerbations on lung function in dupilumab-treated patients: LIBERTY ASTHMA QUEST
Bibliographic record
Abstract
Background: Severe asthma exacerbations can rapidly impair lung function. Dupilumab (DPL), a fully human mAb, blocks the shared receptor component for IL-4/IL-13, key and central drivers of type 2 (T2) inflammation. In phase 3 QUEST (NCT02414854), add-on DPL 200/300mg every 2 weeks vs placebo (PBO) reduced severe exacerbations and improved FEV1 in patients (pts) with uncontrolled, moderate-to-severe asthma with ≥1 exacerbation prior to enrollment. DPL was generally well tolerated. Effects of DPL were greater in pts with elevated T2 biomarkers at baseline (BL). Aim: To assess the impact of severe exacerbations on post-bronchodilator (BD) FEV1 in QUEST pts with T2 (≥150 eosinophils [eos]/µL and/or FeNO ≥25ppb) or T2-high asthma (≥300eos/µL and/or FeNO ≥25ppb) at BL. Methods: Change from BL in post-BD FEV1 after the first severe exacerbation was assessed post hoc in DPL vs PBO pts censoring data at onset of potential second severe exacerbation. Results: Post-BD FEV1 recovered faster after an exacerbation in pts on DPL vs PBO (Figure). Benefits were greater within 6 weeks of exacerbation and were sustained over time. Pts with T2-high asthma at BL benefited most from DPL. Conclusion: DPL vs PBO reduced impact of exacerbations on post-BD FEV1 in pts with moderate-to-severe asthma and elevated T2 biomarkers at BL. Improvement was rapid and suggests a sustained response to DPL and lung function recovery after an exacerbation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".