Rationale, Criteria, and Impact of Identifying Extreme Risk in Patients with Atherosclerotic Cardiovascular Disease
Bibliographic record
Abstract
Abstract Assessment of the overall risk of atherosclerotic cardiovascular disease (ASCVD) is the first step in managing dyslipidemia and is an important reference for the target and intensity of treatment. Recently, different guidelines and consensuses on the management of this condition have successively recommended further risk stratification among patients with ASCVD, and a new “extreme risk” category has been proposed to identify patients who may obtain greater benefit from more intensive lipid-lowering therapy. The definition and terminology of extreme risk varies among different guidelines and consensuses; however, they all recommended an aggressive lipid-lowering therapeutic approach and/or a more stringent low-density lipoprotein cholesterol target for patients at extreme risk. Regardless of the definitions, this general approach may have a remarkable effect on the treatment of this condition in clinical practice. To help clinicians and patients to better understand the new strategy for the secondary prevention of ASCVD, this review provides a summary highlighting the necessity of further risk stratification among ASCVD patients, how patients at extreme risk can be identified, and the potential impact of applying the new “extreme risk” category in clinical practice.
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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".