Relation between reduction in fractional exhaled nitric oxide and efficacy in asthma patients treated with dupilumab
Bibliographic record
Abstract
Background: FeNO, a biomarker that detects interleukin (IL) -4 and -13–mediated inflammation, is predictive of higher exacerbation rates in placebo treated patients. During QUEST, dupilumab, which blocks the receptor for IL-4/−13, reduced severe asthma exacerbations, improved lung function and was generally well tolerated in asthma patients. Aims: Evaluation of the relation between FeNO change from baseline at Week 52 and efficacy in subgroups of patients with type 2 asthma at baseline in QUEST. Methods: Patients with baseline blood eosinophils ≥ 150 cells/µL or FeNO ≥ 20 ppb and patients with FeNO ≥ 25 ppb were analyzed. Annualized rate of severe exacerbations (AER) in the 52-week treatment period and change from baseline in pre-bronchodilator FEV1 at Week 52 were derived as function of FeNO change at Week 52 using spline models. Results: The dupilumab subgroups showed lower AERs, compared to placebo. Greater improvements in FEV1 at Week 52 were associated with greater reductions in FeNO at Week 52 in dupilumab and placebo, although patients treated with dupilumab showed higher lung function results compared to placebo (Fig). Conclusions: Dupilumab lowered the AER versus placebo, regardless of the reduction in FeNO. Greater reductions in FeNO (at Week 52) were associated with greater improvements in FEV1 at Week 52 with dupilumab treated patients, versus placebo, displaying enhanced lung function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".