Efficacy and safety of add-on dupilumab vs inhaled corticosteroid (ICS) dose escalation in patients with asthma uncontrolled on medium-dose ICS/long-acting β2-agonist (LABA) – AIM4: Next Step study
Bibliographic record
Abstract
Background: GINA 2024 guidance document recommends increasing ICS for patients with asthma uncontrolled on medium-dose ICS/LABA before add-on biologic therapy, despite acknowledging limited benefit for most patients and increased risk of systemic side effects. Aims and objectives: AIM4: Next Step ( NCT06572228 ) will evaluate the efficacy and safety of dupilumab as add-on therapy to medium-dose ICS/LABA vs ICS dose escalation in patients with uncontrolled asthma. Methods: A randomized, double-blind, active-controlled, parallel-group study in patients aged 12−80 y with asthma uncontrolled on medium-dose ICS/LABA. Key eligibility criteria: pre-bronchodilator FEV1 50–80% of predicted, FEV1 reversibility ≥12% and 200 mL, 5-item Asthma Control Questionnaire (ACQ-5) score ≥1.5, blood eosinophil count ≥300 cells/µL (90% of population), and ≥1 severe exacerbation in prior year. Patients will be randomized (1:1) to receive either dupilumab added on to medium-dose ICS/LABA or placebo plus escalation to high-dose ICS/LABA for 52 Wk. Results: Primary endpoint is annualized severe exacerbation rate over 52 Wk. Key secondary endpoints: change from baseline to Wk 12 in pre-bronchodilator FEV1 and ACQ-5 score, patient proportion achieving ACQ-5 <1.5 at Wk 12, and annualized cumulative dose of SCS exposure for severe asthma exacerbations at Wk 52. Safety outcomes include treatment-emergent adverse event incidence. Conclusions: AIM4: Next Step will assess efficacy and safety of dupilumab as add-on therapy with medium-dose ICS/LABA vs high-dose ICS/LABA escalation alone in patients with uncontrolled asthma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".