Regression of carotid atherosclerosis in high‐risk individuals with proprotein convertase subtilisin/kexin type 9 inhibitors
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors represent a novel approach for reducing cholesterol and, accordingly, the burden of atherosclerosis. However, limited data are available regarding the possible effects of PCSK9 inhibitors on atherosclerotic plaque. To evaluate the efficacy of PCSK9 inhibitors in reducing carotid plaque progression in individuals with high-risk carotid atherosclerotic disease. We used carotid total plaque area (TPA) to assess the burden of atherosclerosis. Ultrasound imaging of the carotid was acquired before and after the initiation of PCSK9 inhibitor therapy. We selected high-risk cases with atherosclerosis with a minimum of three ultrasound examinations, 1 year before, one at the time of initiation of a PCSK9 inhibitor, and 1 year after initiating a PCSK9 inhibitor. Statistical analysis was conducted using the mixed-effects model with Restricted Maximum Likelihood (REML). We reviewed data from 131 patients with a mean follow-up of 6 (±4) years. Patients were high-risk, with the majority having diabetes or hypertension. There was a decrease in TPA, particularly during the first 3 years after initiating PCSK9 inhibitor therapy (p < 0.05). Furthermore, we observed that individuals with higher baseline serum low-density lipoprotein cholesterol (LDL-C) levels experienced a greater decline in TPA (p < 0.05). PCSK9 inhibitors are effective in achieving plaque regression in high-risk patients with atherosclerosis. This is important, as plaque regression is associated with a lower risk of stroke, myocardial infarction, or vascular death.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".