Applying apoB to the diagnosis and therapy of the atherogenic dyslipoproteinemias: a clinical diagnostic algorithm
Bibliographic record
Abstract
PURPOSE OF REVIEW: The first objective is to present the most recent evidence relating to the efficacy of apolipoprotein B as a diagnostic index of the risk of vascular disease and a therapeutic target for statin therapy. The second is to present a diagnostic algorithm for the apolipoprotein B100 dyslipidemias based on triglyceride and apoB. RECENT FINDINGS: The results from several recent prospective epidemiological studies demonstrate apoB to be superior to any of the cholesterol indices to estimate the risk of vascular disease. Similarly, the results of several of the major statin clinical trials demonstrate that apoB is a more adequate index of the adequacy of statin therapy than any of the cholesterol indices. Recent studies of lipoprotein subclass distribution in subjects with familial combined hyperlipidemia are reviewed. They demonstrate the limitations of the original lipid-based criteria and point to the necessity of using apoB as a fundamental diagnostic criterion for the disorder. A diagnostic algorithm for an apoB100 atherogenic dyslipoproteinemias is presented and the limitations of the lipid-based system described. SUMMARY: The evidence supporting the clinical use of apoB is solid, its measurement is standardized, and automated, inexpensive laboratory testing could easily be widely available. However, clinical benefit will only follow clinical application.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".