Adherence and Persistence to Long-Acting Anticholinergics Treatment Episodes in Patients With Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
BACKGROUND: No studies have examined adherence or persistence to long-acting anticholinergics (LAAC) treatment episodes in patients with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To estimate 1-year adherence and 5-year persistence to LAAC during treatment episodes, and the likelihood of initiating a subsequent treatment episode. METHODS: A retrospective cohort of LAAC-treated COPD patients was reconstructed from Quebec databases. A treatment episode was initiated at cohort entry, defined as the first LAAC prescription date on/after the first COPD diagnosis date recorded between October 1, 2003, and March 31, 2014. We identified a subsequent treatment episode up to 5 years after the end of the episode initiated at cohort entry. We measured adherence as the proportion of days covered over 1 year. Persistence was defined as prescription renewal within 90 days of the previous prescription and was plotted using Kaplan-Meier curves over 5 years. The 5-year hazard and cumulative incidence of initiating a subsequent episode were estimated with survival analyses. We compared adherence and persistence between the treatment episodes using t and log-rank tests. RESULTS: The cohort included 113 435 COPD patients. Adherence and persistence to LAAC were significantly lower in the subsequent treatment episode (55% vs 63%; P < 0.0001). The likelihood of initiating a subsequent episode was greatest immediately after the cessation of the initial episode, with 59% of patients starting a subsequent episode within 1 year. CONCLUSION: Adherence and persistence to LAAC were lower in the subsequent treatment episode. Interventions should be offered quickly after LAAC cessation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".