Prescription Pathways from Initial Medication Use to Triple Therapy in Older COPD Patients: A Real-World Population Study
Bibliographic record
Abstract
Background and objective Triple therapy with an inhaled corticosteroid (ICS), a long-acting β2-agonist bronchodilator (LABA) and a long-acting muscarinic antagonist (LAMA) is recommended as step-up therapy for chronic obstructive pulmonary disease (COPD) patients who continue to have persistent symptoms and increased risk of exacerbation despite treatment with dual therapy. We sought to evaluate different treatment pathways through which COPD patients were escalated to triple therapy.Methods We used population health databases from Ontario, Canada to identify individuals aged 66 or older with COPD who started triple therapy between 2014 and 2017. Median time from diagnosis to triple therapy was estimated using the Kaplan-Meier method. We classified treatment pathways based on treatments received prior to triple therapy and evaluated whether pathways differed by exacerbation history, blood eosinophil counts or time period.Results Among 4108 COPD patients initiating triple therapy, only 41.2% had a COPD exacerbation in the year prior. The three most common pathways were triple therapy as initial treatment (32.5%), LAMA to triple therapy (29.8%), and ICS + LABA to triple therapy (15.4%). Median time from diagnosis to triple therapy was 362 days (95% confidence interval:331–393 days) overall, but 14 days (95% CI 12–17 days) in the triple therapy as initial treatment pathway. This pathway was least likely to contain patients with frequent or severe exacerbations (22.0% vs. 31.5%, p < 0.001) or with blood eosinophil counts ≥300 cells/µL (18.9% vs. 22.0%, p < 0.001).Conclusion Real-world prescription of triple therapy often does not follow COPD guidelines in terms of disease severity and prior treatments attempted.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".