Adherence to statins, beta-blockers and angiotensin-converting enzyme inhibitors following a first cardiovascular event: a retrospective cohort study.
Bibliographic record
Abstract
BACKGROUND: Population studies of statin adherence are generally restricted to one to two years of follow-up and do not analyze adherence to other drugs. OBJECTIVES: To report long-term adherence rates for statins, angiotensin-converting enzyme (ACE) inhibitors and beta-blockers in patients who recently experienced a first cardiovascular event. METHODS: Linked administrative databases in the province of Saskatchewan were used in this retrospective cohort study. Eligible patients received a new statin prescription within one year of their first cardiovascular event between 1994 and 2001. Adherence to statins, beta-blockers and ACE inhibitors was assessed from the first statin prescription to a subsequent cardiovascular event. RESULTS: Of 1221 eligible patients, the proportion of patients adherent to statin medications dropped to 60.3% at one year and 48.8% at five years. The decline in the proportion of adherent patients was most notable during the first two years (100% to 53.7%). Several factors were associated with statin adherence, including age (P = 0.012), number of physician service days (P = 0.037), chronic disease score (P = 0.032), beta-blocker adherence (P < 0.001) and ACE inhibitor adherence (P < 0.001). Adherence to beta-blockers and ACE inhibitors was very similar to adherence to statin medications at each year of follow-up. CONCLUSIONS: Patients who exhibit optimal adherence over one to two years after their initial cardiovascular event generally remain adherent over subsequent years. Also, adherence to beta-blockers and ACE inhibitors is significantly associated with statin adherence in a subset of patients; however, overall adherence to all three drugs was similarly poor.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".