The design and rationale of the Advancing Cardiac Care Unit-based Rapid Assessment and Treatment of hypErcholesterolemia (ACCURATE) study
Bibliographic record
Abstract
Familial hypercholesterolemia (FH) is an inherited condition characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels and premature atherosclerotic cardiovascular disease (ASCVD). Despite being the most common inherited cardiovascular disorder, it is still highly underdiagnosed and undertreated worldwide. We designed the Advancing Cardiac Care Unit-based Rapid Assessment and Treatment of hypErcholesterolemia (ACCURATE) study to test the hypothesis that opportunistic genetic testing for FH among patients hospitalized for acute coronary syndrome (ACS) will increase the diagnosis of FH and improve patient outcomes. ACCURATE is a non-randomized, controlled trial of patients <60 years old admitted to an acute cardiac unit with ACS and elevated LDL-C levels. The first cohort will consist of a control group of patients presenting with ACS who will be treated according to usual standard-of-care. The second cohort will consist of patients presenting with ACS in whom research-based genetic testing for FH will be performed during hospitalization and the results returned to the treating physicians. The primary endpoint will be the number of patients with a new diagnosis of FH. The secondary endpoints will be the proportion of patients who undergo intensification of lipid-lowering therapy, the lowest LDL-C level achieved, and the proportion of patients reaching guideline recommended lipid targets in the 12 months after the index ACS. To our knowledge, ACCURATE represents the first clinical trial of genetic testing for FH in the acute cardiac care setting and is expected to help identify optimal approaches to increase the diagnosis and treatment of FH.
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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 | 0.056 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".