Apolipoprotein B: the Rosetta Stone of lipidology
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the evidence that apolipoprotein B (apoB) integrates the conventional lipid markers - total cholesterol, triglycerides, LDL-cholesterol, and non-HDL-cholesterol - into a single index that accurately and simply quantitates the atherogenic risk due to the apoB lipoprotein particles. RECENT FINDINGS: Marked hypertriglyceridemia remains the essential signal for hyperchylomicronemia and potential pancreatitis. However, with the exception of Lp(a) and the abnormal cholesterol-enriched remnant particles that are the hallmark of type III hyperlipoproteinemia, recent evidence from discordance analyses and Mendelian randomization indicate that apoB integrates the risk due to the atherogenic lipoprotein particles because all LDL particles are, within the limits of our ability to measure any differences, equally atherogenic and all, except the largest VLDL particles are, within the limits of our ability to measure any differences, equally atherogenic. SUMMARY: Measuring apoB as well as the conventional lipids is essential for accurate diagnosis. For almost all follow-up, however, apoB is all that need be measured. ApoB is the Rosetta Stone of lipidology because dyslipoproteinemia cannot be understood unless apoB is measured.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".