Bibliographic record
Abstract
PURPOSE OF REVIEW: To compare non-HDL-cholesterol (non-HDL-C) and apolipoprotein B (apoB) as targets for LDL-lowering therapy. The conventional approach is restricted to a comparison of in-trial data. Although essential, this overlooks the issue as to which marker better identifies residual risk after any particular treatment regimen. RECENT FINDINGS: Data from a series of recent studies, including Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin, Measuring Effective Reductions in Cholesterol Using Rosuvastatin Therapy II, Treating to New Targets-Incremental Decrease in End Points Through Aggressive Lipid Lowering and Collaborative Atorvastatin Diabetes Study, demonstrating that apoB better identifies residual risk than non-HDL-C will be reviewed. SUMMARY: Comparing markers such as non-HDL-C and apoB for the accuracy with which they identify risk during a trial is essential but not sufficient. It is also necessary to compare markers for how well they identify residual risk, and, in this regard, apoB clearly outperforms non-HDL-C.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".