Efficacy and Safety of Ezetimibe Added on to Atorvastatin (20 mg) Versus Uptitration of Atorvastatin (to 40 mg) in Hypercholesterolemic Patients at Moderately High Risk for Coronary Heart Disease††Conflicts of interest: Dr. Conard served as a consultant and advisor for Merck & Company and Merck/Schering-Plough. Dr. Bays received research grants from Amylin, San Diego, California, Amgen, Thousand Oaks, California, J&J, Langhorne, Pennsylvania, Aegerion, Bridgewater, New Jersey, Abbott, Chicago, Illinois, Arena Pharmaceuticals, San Diego, California, GlaxoSmithKline (Glaxo), London, UK, Hoffmann LaRoche, Nutley, New Jersey, Merck, Whitehouse Station, New Jersey, MSP, Kenilworth, New Jersey, Metabolex, San Jose, California, Schering-Plough, Kenilworth, New Jersey, Orexigen, San Diego, California, Reliant, Liberty Corner, New Jersey, Sciele, Atlanta, Georgia, Takeda, Osaka, Japan, TAP, Lake Forest, Illinois, and Vivus, Mountain View, California; received speakers' honoraria from Abbott, Daiichi Sankyo, Tokyo, Japan, GlaxoSmithKline, Reliant, Merck & Company, Merck/Schering-Plough, and Schering-Plough; received honoraria from Abbott, AstraZeneca, London, UK (Wilmington, Delaware, US Headquarters), Daiichi Sankyo, GlaxoSmithKline, Reliant, Merck & Company, Merck/Schering-Plough, and Schering-Plough; and served as a consultant and advisor for Abbott, GlaxoSmithKline, Metabolex, San Jose, California, Reliant, Takeda, AstraZeneca, and Essentialis, Carlsbad, California. Dr. Leiter received grants and speakers' honoraria from and served as a consultant and advisor for AstraZeneca, Merck & Company, Merck/Schering-Plough, and Pfizer, New York, New York. Mr. Bird, Mr. Rubino, and Drs. Lowe, Tomassini, and Tershakovec are employees of Merck & Company and may own stock and/or hold stock options in the company.
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".