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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".