Bibliographic record
Abstract
Despite advances in pharmacological therapies to treat and prevent cardiovascular disease, it remains the leading cause of death in Canada. There now exists a large body of evidence demonstrating that reduction of low-density lipoprotein cholesterol (LDL-C) effectively reduces cardiovascular morbidity and mortality. Despite this, a large proportion of patients who would benefit from this intervention are still not achieving the recommended LDL-C levels. The currently available pharmacological agents, especially statins, are very effective but have rare, yet potentially significant, side effects. The likelihood of these side effects is small but does increase with increasing drug dose. As a result, dosages are often not titrated upward because they cannot be tolerated or their side effects are feared by either physicians or patients. Ezetimibe is a new cholesterol absorption inhibitor that is safe and effective in total cholesterol and LDL-C reduction. When used as monotherapy or in combination with a statin, ezetimibe has been shown to reduce LDL-C by an additional 15% to 20% and improve high-density lipoprotein cholesterol and triglycerides slightly. The addition of ezetimibe to a statin produces an LDL-C reduction of similar magnitude to a three-fold increase in statin dose. The combination of ezetimibe and either atorvastatin or simvastatin has also been found to be beneficial in patients with homozygous familial hypercholesterolemia. The safety profile is similar to placebo and no significant drug interactions have been observed. There is no clinical trial outcome evidence associated with the use of ezetimibe at this time. Thus, ezetimibe is a safe and effective addition to the current LDL-C lowering regimen and is most useful in those patients who cannot achieve sufficient LDL-C reduction with an adequate dose of statin alone, cannot tolerate a statin or are fearful of a statin.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".