Aortic stenosis and lipids: does intervention work?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Aortic stenosis is a common disorder affecting increasing numbers of elderly patients, which is associated with markedly increased cardiovascular morbidity and mortality. Both histological and epidemiological studies have suggested that the process of valve degeneration may resemble atherosclerosis. This review investigates the potential role of lipid-lowering therapy in its management. RECENT FINDINGS: The current treatment methods are surgical or endovascular intervention but it has been hoped that antiatherosclerotic therapies might result in a reduction in progression and need for intervention. There have been a number of retrospective studies of lipid-lowering therapy, which suggested that statins might be effective in reducing progression of aortic stenosis. Lately a series of trials of aggressive statin therapy on surrogate markers of progression have shown no benefit from these measures. The controversial Simvastatin-Ezetimibe and Aortic Stenosis (SEAS) endpoint trial did not show any benefit on a combined endpoint of valve-related and ischaemic events but hinted at a reduction in cardiovascular events in this high cardiovascular risk group. SUMMARY: The data clearly shows statins have little effect in well established stenotic disease with calcification, but their effects earlier in the disease process (aortic valve sclerosis) remain to be determined.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".