Low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol and apolipoprotein B for cardiovascular care
Bibliographic record
Abstract
PURPOSE OF REVIEW: Some experts and consensus groups continue to argue that apolipoprotein B (apoB) should not be introduced broadly into clinical care. But, too often, the present approach to clinical care is not succeeding. An important reason for this failure, we believe, is that the conventional approach limits what the expert clinician can accomplish and is too complex, confusing, and contradictory for primary care physicians to apply effectively in their practise. RECENT FINDINGS: There are four major reasons that apoB should be measured routinely in clinical care. First, apoB is a more accurate marker of cardiovascular risk than LDL-C or non-HDL-C. Second, the measurement of apoB is standardized whereas the measurements of LDL-C and non-HDL-C are not. Third, with apoB and a conventional lipid panel, all the lipid phenotypes can be simply and accurately distinguished. This will improve the care of the expert. Fourth, apoB, as the single measure to evaluate the success of therapy, would simplify the process of care for primary care physicians. SUMMARY: By introducing apoB broadly into clinical care, the process of care will be improved for both the expert and the primary care physician, and this will improve the outcomes of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".