Improving medication adherence in patients with cardiovascular disease: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate and compare the effect of interventions for improving adherence to medications for atherosclerotic cardiovascular disease (ASCVD) secondary prevention. METHODS: We extracted eligible trials from a 2014 Cochrane systematic review on adherence for any condition. We updated the search from CENTRAL, Medline, Embase, PsycINFO, CINAHL, Sociological Abstracts and trial registers through November 2016. Study reports needed to be from a randomised controlled trial, incorporate participants identified as having ASCVD and interventions aimed at improving adherence to medicines for secondary prevention of ASCVD and measure both adherence and a clinical outcome. Two reviewers independently determined the eligibility of studies, extracted data and conducted a narrative synthesis. RESULTS: We identified 17 trials (n=17 448 participants). Most trials had high risk of bias in at least one domain. The intervention group adherence rates ranged from 44%to99% and the comparator group adherence rates ranged from 13% to 96%. Three distinct interventions reported improvements in both adherence and clinical outcomes: short message service (65% vs 13% of participants with high adherence in the intervention vs control group), a fixed-dose combination pill (86% vs 65% adherence, risk ratio of being adherent, 1.33; 95% CI 1.26 to 1.41) and a community health worker-based intervention (97% in the intervention group compared with 92% in the control group; OR=2.62, 95% CI 1.32 to 5.19). CONCLUSIONS: We identified three interventions that demonstrated improvements in adherence and clinical outcomes. Ongoing, longer-term trials will help determine whether short-term changes in adherence can be maintained and lead to differences in clinical events.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".