Non-HDL C equals apolipoprotein B: except when it does not!
Bibliographic record
Abstract
PURPOSE OF REVIEW: Whether national guidelines should incorporate apolipoprotein B (apoB) into clinical practice is one of the most important and contentious decisions they must face. Canada has chosen to do so. What Europe and America decide remains to be seen. RECENT FINDINGS: Obviously, the results of the major epidemiological studies and clinical trials should be major drivers of decisions about guidelines. Such evidence clearly indicates that apoB is superior to LDL C as a marker of risk and an index of the adequacy of therapy but is mixed as to whether apoB is superior to non-HDL C. In this paper, we demonstrate that the issue is more complicated than it appears: that even if non-HDL C and apoB are equal predictors of vascular risk (which we do not believe is the case), this is not due to the VLDL C that is included in non-HDL C but rather reflects the fact that non-HDL C is a 'backwards' measure of apoB - that is, non-HDL C provides an indirect estimate of LDL particle number. Moreover, equal predictive power in groups does not mean that markers have equal predictive power in individuals. We also list multiple clinical circumstances when non-HDL C and apoB lead to different clinical decisions because the real test of markers is when they differ, not when they agree. SUMMARY: Thus, our conclusion is that apoB and non-HDL C are equal - except when they are not. Because apoB allows greater specificity of diagnosis and therapy, it re-establishes the primacy of individuals over groups as the objects of our study and our care and that may be its most important contribution to clinical lipidology.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".