Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss recent findings on the implications of statin discontinuation. RECENT FINDINGS: The beneficial effects of statins in decreasing inflammatory markers, cardiovascular events, cardiovascular mortality, and all-cause mortality in patients with and without a history of cardiovascular disease have been underscored in past and recent studies. However, patients often do not adhere to their statin therapy. Discontinuation rates, though improved over time, remain high not only in primary but also in secondary prevention patients in the clinical practice. Recent studies have found that discontinuing statins, particularly after acute events (e.g. acute myocardial infarction or stroke), has a harmful effect on cardiovascular outcomes and all-cause mortality; patients who discontinued their statin therapy had worse outcomes than those who were never prescribed statins. This could be attributed to a biological rebound phenomenon. SUMMARY: Statin therapy has a number of beneficial effects on patient outcomes and should be prescribed according to current cardiovascular disease guidelines. Importantly, statin discontinuation is associated with harmful outcomes. Clinicians should become more aware of these effects and counsel their patients to adhere to their statin therapy. Current evidence suggests that, unless contraindicated, statins should not be discontinued, especially after an acute vascular event.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".