Abstract 3110: Adherence to Beta-blockers and ACE Inhibitors/Angiotensin Receptor Blockers in the First Year after Diagnosis of Heart Failure: 10 Year Observational Trends
Bibliographic record
Abstract
Introduction: As prescriptions for evidence based medications in patients with heart failure (HF) have increased over the past 10 years, we aimed to determine if adherence to HF medications has also increased over this time. Methods: A retrospective cohort was created using administrative databases from the province of Saskatchewan, Canada. Subjects discharged alive from their first hospitalization for HF between 1994 and 2003 were eligible for study. Those filling a prescription for a beta blocker (BB), ACE inhibitor (ACEI), or angiotensin receptor blocker (ARB) within six months after discharge were selected. The proportion of subjects with optimal 1-year adherence (≥ 80%) was determined and divided according to the year of entry into the study. Results: Of 8,805 eligible patients, 67% of BB users (941/1414) and 74% of ACEI/ARB users (4441/5991) exhibited 1-year adherence ≥ 80%. When grouped by year of initial HF hospitalization, the proportion of patients with optimal 1-year adherence improved from 54% to 75% with BB and from 67% to 80% with ACEI/ARBs between 1994/95 and 2002/03 [Figure ]. Mean 1-year adherence improved from 71% to 83% and 80% to 88% for BB and ACEI/ARBs, respectively. After covariate adjustment using multivariate logistic regression, year of initial HF hospitalization remained independently associated with optimal 1-year adherence. Subjects discharged in 2003 were significantly more likely to exhibit optimal adherence to a BB (OR 2.04; 95% CI 1.21–3.44) or an ACEI/ARB (OR 1.65; 95% CI 1.30–2.08) than those prescribed therapy in 1994/95. Conclusion: One year adherence to BB and ACEI/ARB is improving over time in patients newly diagnosed with HF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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".