Landscape of Lipid Management Following an Acute Coronary Syndrome Event: Survey of Canadian Specialists
Bibliographic record
Abstract
BACKGROUND: Following the occurrence of an acute coronary syndrome (ACS), patients are at high risk for subsequent cardiovascular events. Therapies to lower the level of low-density lipoprotein (LDL) cholesterol remain a pillar in secondary prevention approaches following ACS. Significant variability remains in the application of therapies to lower cholesterol level in clinical practice. METHODS: A cross-sectional, online survey was conducted of 200 cardiovascular and lipid specialists across Canada who routinely care for patients following the occurrence of ACSs. The survey consisted of 50 multiple-choice questions with opportunities for free-text entry exploring knowledge of lipid guidelines and recent clinical trials, and in-hospital and outpatient management of lipids and familial hypercholesterolemia. RESULTS: A total of 67.5% (n = 135) of participants stated that a lipid panel would routinely be obtained during the first 24 hours of an admission for an ACS, and 68.5% (n = 137) stated that their hospitals had standing orders for statin initiation at ACS presentation. In high-risk patients, the majority (75.5%; n = 151) of participants indicated that they target an LDL cholesterol level of <1.8 mmol/L. However, a subset (22%; n = 44) would target lower LDL cholesterol levels ranging from 0.5 to 1.7 mmol/L. Only 32.0% (n = 64) of participants stated that >70% of their ACS patients were at or below guideline-recommended LDL cholesterol levels. Respondents generally underappreciated the prevalence of familial hypercholesterolemia in both the general population and ACS patients. CONCLUSIONS: There is significant variation in practice patterns involving therapies to lower LDL cholesterol level in the post-ACS onset period. To improve management of lipids in this high-risk population, changes to institutional policies, shared responsibility of lipid management across multiple disciplines, and physician education are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".