Achievement of recommended lipid and lipoprotein levels with combined ezetimibe/statin therapy versus statin alone in patients with and without diabetes
Bibliographic record
Abstract
Treatment guidelines identify low-density lipoprotein cholesterol (LDL-C) as the primary target of therapy with secondary targets of non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (apoB). Data were pooled from 27 randomised, double-blind, active or placebo-controlled trials in 21,794 adult hypercholesterolaemic patients (LDL-C 1.81-6.48 mmol/L) receiving ezetimibe/statin or statin for 4-24 weeks. Percentages of patients achieving various targets were calculated among diabetes (n = 6541) and non-diabetes (n = 15,253) subgroups. Significantly more patients with and without diabetes achieved specified levels of LDL-C (< 2.59, < 1.99, < 1.81 mmol/L), non-HDL-C (< 3.37, < 2.59 mmol/L) and apoB (< 0.9, < 0.8 g/L) with ezetimibe/statin versus statin. Patients with diabetes had larger mean per cent reductions in LDL-C and non-HDL-C than non-diabetes patients. A greater percentage of patients achieved both the LDL-C and apoB targets and all three LDL-C, apoB, and non-HDL-C targets with ezetimibe/statin versus statin in both subgroups. Patients with diabetes benefitted at least as much as, and sometimes more than, non-diabetes patients following treatment with ezetimibe/statin.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".