Impact of a Better Adherence to Antihypertensive Agents on Cerebrovascular Disease for Primary Prevention
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The benefits of antihypertensive (AH) drugs on the risks of major cardiovascular outcomes have been demonstrated in clinical trials. However, approximately half of hypertensive patients do not adhere well to their prescribed AH therapy in actual practice. The purpose of this study was to assess the impact of adherence to AH agents on the incidence of cerebrovascular disease (CD) in real-world practice. METHODS: A cohort of 83 267 hypertensive patients was reconstructed from the Régie de l'assurance maladie du Québec databases. Subjects included were between 45 and 85 years old, initially free of cardiovascular disease, and newly treated for hypertension with AH agents between 1999 and 2004. A nested case-control design was conducted to study CD occurrence. Every case was matched for age and duration of follow-up with up to 15 randomly selected control subjects. The adherence to AH drugs was measured by calculating the medication possession ratio. Conditional logistic regression models were performed to assess the association between adherence to AH agents and CD adjusting for various potential confounders. RESULTS: At cohort entry, the mean patient age was 65 years, 37.3% were male, 8.6% had diabetes, and 19.5% had dyslipidemia. High adherence (>/=80%) to AH drugs significantly decreased the risk of CD by 22% (rate ratio, 0.78; 95% CI, 0.70 to 0.87) compared with lower adherence. Male gender, occurrence of cardiovascular disease during follow-up, and dyslipidemia were risk factors for CD. CONCLUSIONS: High adherence to AH therapy is associated with a reduced risk of CD outside the context of clinical trials in primary prevention.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".