Low-density lipoprotein lowering in type 2 diabetes mellitus: how to know how low to go
Bibliographic record
Abstract
PURPOSE OF REVIEW: Getting low-density lipoprotein to the right level in patients with type 2 diabetes should be relatively easy, given the potent pharmacological therapy that is available and the fact that low-density lipoprotein C is typically normal in these patients. Getting it right means getting the target for therapy right, however. The article examines the criteria that should be used to make this choice. RECENT FINDINGS: In comparing different parameters, three criteria, in particular, need to be taken into account: the on-treatment predictive value of any parameter; the value relative to the population of one parameter compared with another, namely which is more deviant from the norm; and the concurrent level of high-density lipoprotein. The evidence from the low-density lipoprotein-lowering trials indicates that low-density lipoprotein C is not nearly as good as non-high-density lipoprotein C as a guide for the adequacy of low-density lipoprotein lowering, or, better still, apoB, with the apoB/apoA-I ratio being clearly the best of all. SUMMARY: The evidence from the major clinical trials indicates the best single index of the adequacy of low-density lipoprotein lowering is the apoB/apoA-I ratio. Clinical practice should adapt to clinical evidence and, therefore, guidelines should be based on apolipoproteins rather than the conventional cholesterol indices.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".