Abstract P122: The Clinical Impact of TC/HDL-C Discordance With LDL-C and Non-HDL-C: 20 Year Follow-up of the Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Introduction: TheTotal to High-Density Lipoprotein cholesterol (TC/HDL-C) ratio may carry unique prognostic information for atherosclerotic cardiovascular diseases (ASCVD) risk beyond the more commonly used measures of low-density lipoprotein (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C). Information regarding patient-level discordance between these lipid parameters may be clinically relevant in refining risk stratification. Methods: We studied 14,393 ARIC participants free of ASCVD at baseline who were mean age 54 years, 56% women, and 25% black. Lipids were measured at baseline (1987-1989) and at four additional visits. TC/HDL-C discordance with non-HDL-C and LDL-C (defined by categories above/below the median) was updated at each visit. Participants were followed for ASCVD events through end of 2013, with mean follow-up of 20 years. Multivariable-adjusted Cox hazard models were used to estimate for ASCVD risk for each category. Results: Among participants with low LDL-C (Table). Conclusions: In a primary prevention population,the TC/HDL-C ratio provides additional prognostic information to non-HDL-C and LDL-C. Individuals who reach low levels of LDL-C or non-HDL-C may still be at high risk of ASCVD if TC/HDL-C is discordantly high.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".