Implementing Dyslipidemia Guidelines into Clinical Practice Following an Acute Coronary Syndrome: Challenges and Opportunities for Improvement
Bibliographic record
Abstract
Following an acute coronary syndrome (ACS), patients remain at a residual increased risk of adverse cardiovascular events. As such, secondary prevention strategies, including dyslipidemia management, are key in the delivery of post-ACS care. Multiple randomized controlled trials have highlighted the benefit of lipid-lowering therapies in reducing low-density lipoprotein cholesterol levels, an independent predictor of adverse cardiovascular events post-ACS. However, registries have demonstrated that post-ACS, a significant proportion of patients are not achieving guideline-recommended low-density lipoprotein target levels, and intensification of lipid-lowering therapies continues to be underutilized. This review assesses strategies in which post-ACS lipid management can be improved, in particular by standardizing follow-up care through dedicated post-ACS clinics.
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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 | 0.057 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".