Predictive factors of non-adherence to secondary preventative medication after stroke or transient ischaemic attack: A systematic review and meta-analyses
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Non-adherence to secondary preventative medications after stroke is relatively common and associated with poorer outcomes. Non-adherence can be due to a number of patient, disease, medication or institutional factors. The aim of this review was to identify factors associated with non-adherence after stroke. METHOD: We performed a systematic review and meta-analysis of studies reporting factors associated with medication adherence after stroke. We searched MEDLINE, EMBASE, CINAHL, PsycINFO, CENTRAL and Web of Knowledge. We followed PRISMA guidance. We assessed risk of bias of included studies using a pre-specified tool based on Cochrane guidance and the Newcastle-Ottawa scales. Where data allowed, we evaluated summary prevalence of non-adherence and association of factors commonly reported with medication adherence in included studies using random-effects model meta-analysis. FINDINGS: From 12,237 titles, we included 29 studies in our review. These included 69,137 patients. The majority of included studies (27/29) were considered to be at high risk of bias mainly due to performance bias. Non-adherence rate to secondary preventative medication reported by included studies was 30.9% (95% CI 26.8%-35.3%). Although many factors were reported as related to adherence in individual studies, on meta-analysis, absent history of atrial fibrillation (OR 1.02, 95% CI 0.72-1.5), disability (OR 1.27, 95% CI 0.93-1.72), polypharmacy (OR 1.29, 95% CI 0.9-1.9) and age (OR 1.04, 95% CI 0.96-1.14) were not associated with adherence. DISCUSSION: This review identified many factors related to adherence to preventative medications after stroke of which many are modifiable. Commonly reported factors included concerns about treatment, lack of support with medication intake, polypharmacy, increased disability and having more severe stroke. CONCLUSION: Understanding factors associated with medication taking could inform strategies to improve adherence. Further research should assess whether interventions to promote adherence also improve outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it