The emerging role of proprotein convertase subtilisin/kexin type-9 inhibition in secondary prevention
Bibliographic record
Abstract
PURPOSE OF REVIEW: The recent advent of a highly efficacious class of low-density lipoprotein cholesterol (LDL-C) lowering agents, the proprotein convertase subtilisin/kexin type-9 (PCSK9) inhibitors, has transformed dyslipidaemia management in patients with cardiovascular disease as well as those with familial hypercholesterolemia. RECENT FINDINGS: Recent positive results of the landmark Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk cardiovascular outcome trial with evolocumab as an add-on to statin therapy demonstrate further reduction of cardiovascular events. Additional safety outcomes from this large randomized trial, as well as the EBBINGHAUS substudy, allay fears of neurocognitive disorder as an adverse effect of achieving very low LDL-C levels with these agents. SUMMARY AND IMPLICATIONS: Widespread clinical adoption of PCSK9 inhibitors will depend on the results from ongoing and planned cardiovascular efficacy and safety trials with PCSK9 inhibitors. In addition, understanding the practical challenges and barriers to usage of these injectable agents by high cardiovascular risk patients will also affect clinical adoption of this class of agents. Analysis of cost-benefit models, along with anticipated updates to practice guidelines for dyslipidaemia management are likely to strengthen the clinical utility of PCSK9 inhibitors. Importantly, the potency of this new class of agents provides a huge opportunity to extend further the 'lower LDL-C is better' hypothesis in an effort to reduce rates of cardiovascular morbidity and mortality on a population level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".