Abstract P235: Implications of the Selection of Target Markers for LDL Lowering
Bibliographic record
Abstract
Our aim was to compare the implications of targeting LDL-lowering treatment to LDL-C, non-HDL-C, or apoB based on a recent meta-analysis of all published epidemiological studies with all three markers' vascular risk associations which found overall per standard deviation relative risk ratios (RRR) of 1.25 for LDL-C, 1.31 for non-HDL-C, and 1.41 for apoB. Our approach was to project the 10-year incidence of CHD events from NHANES 2005-2006 with 1697 subjects representing over 190 million adult US residents under different scenarios defined by the target marker and the percentage of people treated. Framingham equations were used to estimate each subject's 10-year CHD risk. We estimated each subject's risk if treated by dividing their initial risk estimate by the marker's RRR exponentiated to the number of standard deviations (LDL-C: 35 mg/dl, non-HDL-C: 42 mg/dl, apoB: 27 mg/dl) in 40% of the marker's measured level. The potential number of CHD cases prevented by the treatment was calculated by multiplying the difference between initial and treated risk by the number of people represented. The mean 10-year CHD risk was 7.00% indicating 13.3 million incident CHD cases would be expected over the subsequent 10 years with no treatment change. The expected numbers of incident CHD cases prevented under different treatment scenarios are shown in the figure. These results support recommendations to use apoB in clinical practice to identify candidates for LDL-lowering and to target their treatment.
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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 | 0.053 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".