Bibliographic record
Abstract
BACKGROUND: Although the ezetimibe-statin combination has been shown to reduce LDL cholesterol by 12% compared to a statin alone, its effect on hard clinical endpoints such as mortality is less certain. Prior trials evaluated this combination in highly select population groups, but impact on all- cause mortality in the general population has not been reported. METHODS: A total of 3,827 subjects who were prescribed either a statin (group 1) or the combination of statin with ezetimibe (group 2) between January 1st, 2005 and January 1st, 2008 were studied. Socio-demographic and clinical variables and mortality records were analyzed. Univariate and stepwise multivariate logistic regression analysis was performed to identify the impact of ezetimibe on all-cause mortality, controlling for patient characteristics, selected cardiovascular diseases and risk factors, and medications. RESULTS: Group 1 (n = 2,909), and group 2 (n = 918) were similar in regards to most demographic variables, 152 patients died from any cause during the study period. There was no difference in all cause mortality between the groups. Hypertension, higher HDL-C and omega-3 fatty acid use were associated with ezetimibe use in this cohort of patients and were considered as covariates in the analysis. Patients on the drug combination did not experience lower mortality after controlling for covariates and other significant risk factors. CONCLUSIONS: No significant mortality benefit was found with the use of ezetimibe in combination with a statin over use of a statin alone. Omega-3 fatty acid use and higher HDL-C demonstrated a substantial survival benefit.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".