Detection of atherosclerotic cardiovascular disease influences the perceived need for aggressive lipid management
Bibliographic record
Abstract
BACKGROUND AND AIMS: Overt atherosclerotic cardiovascular disease (ASCVD) warrants aggressive lipid lowering. Imaging for ambiguous symptoms suggesting ischemia or for clarification of CV risk in asymptomatic individuals often uncovers previously unknown ASCVD. Guidelines do not provide clear recommendations for aggressive lipid lowering in such cases. We explored physicians' perception, as influenced by tests that detect ASCVD, regarding appropriateness of getting to lipid goals and for theoretically accessing proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i). METHODS: A questionnaire was developed including cases of low to high CV risk, chronic kidney disease (CKD) or type 2 diabetes mellitus (T2DM). Each case was considered with or without angina symptoms and, in turn, whether testing identified previously unknown advanced, early/subclinical or no ASCVD. Synthesis of responses was facilitated by using a scale for perceived appropriateness from 1 (lowest) to 9 (highest). RESULTS: Getting to goal and, if not achieved by statins and/or ezetimibe, accessing PCSK9i was considered appropriate in patients with T2DM with preclinical or advanced ASCVD, patients with moderate or high CV risk and advanced ASCVD, patients with CKD or low CV risk with angina symptoms and advanced ASCVD. For most of the remaining cases adding PCSK9i was considered only possibly appropriate. CONCLUSIONS: Physicians' perception of appropriateness for achieving lipid goals, including access to PCSK9i, is markedly influenced by detection of previously unknown ASCVD. Since these commonly encountered scenarios do not clearly meet current indications for PCSK9i, our data identify pressing areas requiring further research.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".