Are post-treatment low-density lipoprotein subclass pattern analyses potentially misleading?
Bibliographic record
Abstract
BACKGROUND: Some patients administered cholesterol-lowering therapies may experience an increase in the proportion of small LDL particles, which may be misinterpreted as a worsening of atherosclerotic coronary heart disease risk. This study assessed the lipid effects of adding ezetimibe to atorvastatin or doubling the atorvastatin dose on low-density lipoprotein cholesterol (LDL-C) levels (and the cholesterol content of LDL subclasses), LDL particle number (approximated by apolipoprotein B), and LDL particle size. This was a multicenter, double-blind, randomized, parallel-group study of hypercholesterolemic, high atherosclerotic coronary heart disease risk patients. After stabilization of atorvastatin 40 mg, 579 patients with LDL-C >70 mg/dL were randomized to 6 weeks of ezetimibe + atorvastatin 40 mg or atorvastatin 80 mg. Efficacy parameters included changes from baseline in LDL-C, apolipoprotein B, non-high-density lipoprotein cholesterol (non-HDL-C), and lipoprotein subclasses (Vertical Auto Profile II) and pattern for the overall population, as well as patient subgroups with baseline triglyceride levels <150 mg/dL or ≥150 mg/dL. RESULTS: Both treatments significantly reduced LDL-C (and the cholesterol content of most LDL subfractions [LDL1-4]) apolipoprotein B, non-HDL-C levels, but did not reduce the proportion of smaller, more dense LDL particles; in fact, the proportion of Pattern B was numerically increased. Results were generally similar in patients with triglyceride levels <150 or ≥150 mg/dL. CONCLUSIONS: When assessing the effects of escalating cholesterol-lowering therapy, effects upon Pattern B alone to assess coronary heart disease risk may be misleading when interpreted without considerations of other lipid effects, such as reductions in LDL-C, atherogenic lipoprotein particle concentration, and non-HDL-C levels. TRIAL REGISTRATION: (Registered at clinicaltrials.gov: Clinical trial # NCT00276484).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".