Geographic variation in outcomes in RCTs of biologic therapies in patients with asthma or COPD: A systematic literature review
Bibliographic record
Abstract
Background: Geographic variation in exacerbation reductions has been reported in several RCTs, most recently in Phase III SWIFT-1/2 trials of depemokimab, an ultra-long-acting biologic enabling twice-yearly dosing for asthma, with high placebo response in Eastern Europe. Aims: This SLR explored geographic variation in outcomes to respiratory biologic therapies in asthma or COPD RCTs and open-label extension studies (OLEs), helping to identify patterns to assist with data interpretation and clinical applications. Methods: A systematic search from inception of MEDLINE, Embase, EBM Reviews, and key conferences (latter 2019–2024) identified RCTs and OLEs on biologic therapies in patients with severe asthma/COPD reporting outcomes by geography. Screening followed a predefined protocol, with qualitative synthesis of results. Results: Exacerbations by geography were reported in 4 asthma RCTs and 1 OLE, with smaller treatment effects seen in Eastern Europe for most biologics versus placebo; 2 COPD RCTs showed a smaller treatment effect of dupilumab on exacerbations in Eastern Europe versus other regions (Figure). Conclusions: Geographic variation in outcomes was observed across biologic therapies in asthma or COPD RCTs and OLEs, with lower treatment effects in Eastern Europe and higher treatment effects in Asia Pacific (asthma only) versus other regions. Funding: GSK (223319). erj;66/suppl_69/PA2470/F1 F1 F1
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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 | 0.060 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".