Patient-Level Discordance in Population Percentiles of the Total Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Comparison With Low-Density Lipoprotein Cholesterol and Non–High-Density Lipoprotein Cholesterol
Bibliographic record
Abstract
BACKGROUND: The total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C) ratio, estimated low-density lipoprotein cholesterol (LDL-C), and non-HDL-C are routinely available from the standard lipid profile. We aimed to assess the extent of patient-level discordance of TC/HDL-C with LDL-C and non-HDL-C, because discordance suggests the possibility of additional information. METHODS AND RESULTS: We compared population percentiles of TC/HDL-C, Friedewald-estimated LDL-C, and non-HDL-C in 1 310 432 US adults from the Very Large Database of Lipids. Lipid testing was performed by ultracentrifugation (Vertical Auto Profile, Atherotech, AL). One in 3 patients had ≥25 percentile units discordance between TC/HDL-C and LDL-C, whereas 1 in 4 had ≥25 percentile units discordance between TC/HDL-C and non-HDL-C. The proportion of patients with TC/HDL-C > LDL-C by ≥25 percentile units increased from 3% at triglycerides <100 mg/dL to 51% at triglycerides 200 to 399 mg/dL. On a smaller scale, TC/HDL-C > non-HDL-C discordance by ≥25 percentile units increased from 6% to 21%. In those with <15th percentile levels of LDL-C (<70 mg/dL) or non-HDL-C (<93 mg/dL), a respective 58% and 46% were above the percentile-equivalent TC/HDL-C of 2.6. Age, sex, and directly measured components of the standard lipid profile explained >86% of the variance in percentile discordance between TC/HDL-C versus LDL-C and non-HDL-C. CONCLUSIONS: In this contemporary, cross-sectional, big data analysis of US adults who underwent advanced lipid testing, the extent of patient-level discordance suggests that TC/HDL-C may offer potential additional information to LDL-C and non-HDL-C. Future studies are required to determine the clinical implications of this observation. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01698489.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".