Evaluating the Effectiveness of Triple Therapy in Chronic Obstructive Pulmonary Disease Patients: An Asian Population-Based Survey
Bibliographic record
Abstract
Background: The optimal treatment strategy for chronic obstructive pulmonary disease (COPD) remains debated, especially when choosing between triple therapy and long-acting beta agonist (LABA) + long-acting muscarinic antagonist (LAMA). This study aimed to develop a model that simulates real-world prescription patterns and compares the effectiveness of these two treatment options. Methods: This population-based cohort study used Taiwan's National Health Insurance Research Database to follow COPD patients who had been on LABA plus inhaled corticosteroids (ICSs) for more than 28 days. These patients were followed until they either upgraded to triple therapy or switched to LABA plus LAMA. The study enrolled patients from 2013 to 2021. Cox proportional hazard models were used to evaluate the risk of seven individual outcomes, including mortality, COPD exacerbations, acute respiratory failure, pneumonia, and respiratory-related admissions, adjusting for fixed and time-dependent variables. Results: Among the 20,997 included patients (mean (standard deviation (SD)) age: 66.06 (11.54) years; 12,977 males (61.80%)), 16,792 (79.97%) were in the triple therapy group, and 4,205 (20.03%) were in LABA plus LAMA group. The triple therapy group showed significantly higher relative risks in several outcomes: composite outcome (adjusted hazard ratio (aHR): 1.162; 95% confidence interval (CI): 1.098 - 1.230; P < 0.0001), acute respiratory failure (aHR: 1.315; 95% CI: 1.047 - 1.653; P = 0.0186), severe acute exacerbation (aHR: 1.346; 95% CI: 1.078 - 1.682; P = 0.0088), pneumonia (aHR: 1.221; 95% CI: 1.109 - 1.344; P < 0.0001) and respiratory-related admission (aHR: 1.264; 95% CI: 1.157 - 1.382; P < 0.0001), indicating superior effectiveness of LABA plus LAMA in these indicators. Conclusions: In conclusion, in COPD patients, the combination of LABA plus LAMA can significantly improve many major symptoms and reduce the frequency of exacerbations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".