Effect of Number of Medications on Cardiovascular Therapy Adherence
Bibliographic record
Abstract
OBJECTIVE: Increasing regimen complexity is generally assumed to result in lower medication adherence, but there is conflicting evidence. This study determined the relationship between the number of medications dispensed and adherence with chronic cardiovascular regimens. METHODS: A survey was administered to 367 patients who had taken an angiotensin-converting enzyme inhibitor or lipid-lowering medication for at least 3 consecutive months. Information was collected on nonprescription drug use, demographics, adverse effects, and use of adherence aids. Prescription drug use data over the previous 12 months were obtained for each subject from the British Columbia prescription claims database. Adherence for each prescription medication was calculated based on prescription fill dates and number of days supplied. Univariate and multivariate analyses were used to identify predictors of nonadherence (<80%) with cardiovascular medications. RESULTS: Forty-five subjects (12%) were categorized as nonadherent. Nonadherent subjects took fewer regularly scheduled prescription medications per day (4.1 +/- 2.7 vs. 5.9 +/- 3.4; p = 0.001), fewer pills per day (5.3 +/- 3.6 vs. 9.2 +/- 7.1; p < 0.001), and had fewer administration times per day (1.8 +/- 0.7 vs. 2.4 +/- 0.9; p = 0.001). A multivariate logistic regression model adjusting for age, gender, reported adverse effects, reported nonprescription drug use, and use of adherence aids identified fewer regularly scheduled prescription drugs as an independent predictor of nonadherence with chronic cardiovascular medications (OR = 0.85 per medication, 95% CI 0.74 to 0.94; p = 0.01). CONCLUSIONS: Contrary to popular belief, taking fewer medications was associated with lower adherence with chronic cardiovascular regimens.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".