Dupilumab for Chronic Obstructive Pulmonary Disease: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Dupilumab was recently approved to treat eosinophilic phenotypes of chronic obstructive pulmonary disease (COPD). This systematic review aimed to collect and appraise the efficacy and safety of dupilumab to treat patients with COPD. Methods: Databases searched included Ovid Medline, Embase, Web of Science, Directory of Open Access Journals, and International Pharmaceutical Abstracts. Experimental and observational studies, including case reports/series, were eligible for inclusion. Reports were independently screened, appraised, and extracted by three investigators; disagreements were resolved through discussion and agreement. Quality appraisal was conducted using the Cochrane Risk of Bias Tool 2.0, Newcastle–Ottawa Scale, and JBI Checklist for experimental, observational, and case studies, respectively. Results: A total of 307 unique reports were identified, of which 17 were included in this systematic review. The majority (n = 11, 64.7%) of reports presented evidence from the BOREAS and NOTUS trials, the landmark trials serving as the basis for dupilumab’s approval to treat refractory eosinophilic COPD. The results from this systematic review found that dupilumab reduced exacerbations of COPD in patients treated with inhaled triple therapy and it was well tolerated. Conclusions: When added to inhaled triple therapy, dupilumab may decrease patients’ risk for acute exacerbations of COPD. Additional research is necessary to substantiate these findings for broader generalizability, including populations with non-eosinophilic COPD phenotypes.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".