Real-World Effectiveness of Single-Inhaler Triple Therapy for COPD: Impact of Diabetes Comorbidity
Bibliographic record
Abstract
Background: Type 2 diabetes is a frequent comorbidity in chronic obstructive pulmonary disease (COPD) patients, with the GOLD treatment recommendations asserting that the presence of diabetes be disregarded in the choice of treatment.Methods: In a cohort of COPD patients with frequent exacerbations, initiators of single-inhaler triple therapy or dual bronchodilators were compared on the incidence of COPD exacerbation and pneumonia over one year, adjusted by propensity score weighting and stratified by type 2 diabetes.Results: The COPD cohort included 1,114 initiators of triple inhalers and 4,233 of dual bronchodilators (28% with type 2 diabetes).The adjusted hazard ratio (HR) of exacerbation with triple therapy was 1.04 (95% CI: 0.86-1.25)among COPD patients with type 2 diabetes and 0.74 (0.65-0.85) in those without.The incidence of severe pneumonia was elevated with triple therapy among patients with type 2 diabetes (HR 1.77; 1.14-2.75).Conclusion: Triple therapy in COPD is effective among those without, but not those with, type 2 diabetes.Future therapeutic trials in COPD should consider diabetes comorbidity. TWITTER SUMMARYTriple therapy for frequent COPD exacerbators is effective in patients without type 2 diabetes but not in those with type 2 diabetes.The impact of comorbidities should be considered in future COPD therapeutic trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".