Statin Treatment Non-adherence and Discontinuation: Clinical Implications and Potential Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Statins are the most powerful lipid lowering drugs in clinical practice. However, the efficacy of statin therapy, as seen in randomized control trials, is undermined by the documented non-adherence observed in clinical practice. Understanding the clinical consequences of statin non-adherence is an important step in implementing successful interventions aimed at improving adherence. Our previous systematic review included a literature search up to January 2010 on the effects of statin non-adherence or discontinuation on cardiovascular (CV) and cerebrovascular outcomes. We provide an update to this publication and a review of promising interventions that have reported a demonstrated improvement in statin adherence. Through a systematic literature search of PubMed, Ovid Medline, Ovid Embase, CINAHL, Cochrane Library and Web of Science, out of the 3440 initially identified, 13 studies were selected. Non-adherence in a primary prevention population was associated with a graded increase in CV risk. Individuals taking statins for secondary prevention were at particular risk when taking statin with highly variable adherence. Moreover, particular attention is warranted for non-adherence in diabetic and rheumatoid arthritis populations, as non-adherence is significantly associated with CV risk as early as 1 month following discontinuation. Statin adherence, therefore, represents an important modifiable risk factor. Numerous interventions to improve adherence have shown promise, including copayment reduction, automatic reminders, mail-order pharmacies, counseling with a health professional, and fixed-dose combination therapy. Given the complexity of causes underlying statin non-adherence, successful strategies will likely need to be tailored to each patient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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