The Use of Statins in Primary Prevention of Cardiovascular Disease: Benefits versus Risks
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
Background: Cardiovascular disease (CVD) remains a major global health issue. The use of statins in people with a history of CVD is generally well established, however, debate remains about their use for primary prevention in people without CVD. This narrative review aims to present studies related to the benefits and risks of taking statins for primary prevention of CVD. An internet search of the Cochrane Library (2006 to 2021) and PubMed (2006 to 2021) used the following keywords: Hydroxymethylglutaryl-CoA Reductase Inhibitors, statin OR statins; cardiovascular disease, heart disease, coronary disease; primary prevention. Systematic review/ meta- analyses-based articles were included in the review. The studies reported positive outcomes of statins, particularly in relation with reduction in all-cause mortality, non- fatal MI, and non-fatal stroke. Some adverse events were also reported, such as muscle problems, diabetes, liver dysfunctions, and renal and eye disorders, However, the risks attributable to statins were considerably lower and thus did not outweigh the benefits in preventing CVD. It should be acknowledged that the decision to initiate statins for primary prevention should not solely depend on the LDL-C value, but also on overall CVD risk factors for a particular individual, as can be seen in three major guidelines from the American College of Cardiology/ American Heart Association (ACC/AHA) - 2019, Canadian Society of Cardiology (CCS) - 2021, and the European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) - 2019. The risks attributable to statins were relatively low, and thus did not outweigh the benefits in preventing CVD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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