Bibliographic record
Abstract
OBJECTIVE: To review the evidence evaluating the efficacy of statins in reducing the progression of calcified aortic stenosis (AS). DATA SOURCES: MEDLINE, EMBASE, and PubMed were searched (all up to November 2006) for studies evaluating the use of statins to reduce the progression of calcified AS. Search terms included statin, HMG CoA reductase inhibitor, calcified AS, valve stenosis, and calcified stenosis. Additional primary trials were located by searching references noted in review articles. STUDY SELECTION AND DATA EXTRACTION: Clinical trials published in the English language were selected for review. Primary efficacy outcomes evaluated were changes in aortic valve measurements, hemodynamic measures of AS, and change in measures of AS severity. DATA SYNTHESIS: Two prospective clinical trials and 5 retrospective studies were included in this review. All of the retrospective studies demonstrated that statin use was associated with a statistically significant delay in the progression of AS. One prospective observation trial showed benefit of statin use; however, a large, randomized, double-blind, prospective trial showed no benefit of statin use in decreasing the progression of AS. CONCLUSIONS: An association between statin use and a delay in AS progression has been observed in retrospective studies; however, prospective trials showed conflicting results. Currently, statins cannot be recommended for medical treatment of AS until larger trials are conducted.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".