Do Statins have a Role in Reduction/Prevention of Post‐PCI Restenosis?
Bibliographic record
Abstract
The pathophysiology of post-PCI restenosis involves neointimal formation that consists of three phases: thrombosis (within 24 h), recruitment (3-8 days), and proliferation, which starts on day 8 of PCI. Various factors suggested to be predictors/risks for restenosis include C-reactive protein (CRP), inflammatory mediators (cytokines and adhesion molecules), oxygen radicals, advanced glycation end products (AGEs) and their receptors (RAGE), and soluble RAGE (sRAGE). The earlier noted factors produce thrombogenesis, vascular smooth muscle cell proliferation, and extracellular matrix formation. Statins have pleiotropic effects. Besides lowering serum cholesterol, they have various other biological effects including antiinflammatory, antithrombotic, CRP-lowering, antioxidant, antimitotic, and inhibition of smooth muscle cell proliferation. They inhibit matrix metalloproteinase and cyclooxygenase-2, lower AGEs, decrease expression of RAGE and increase levels of serum sRAGE. They also increase the synthesis of nitric oxide (NO) by increasing endothelial NO synthase expression and activity. Preprocedural statin therapy is known to reduce peri- and post-PCI myonecrosis and reduce the need for repeat revascularization. There is evidence that statin-eluting stents inhibit in-stent restenosis in animal models. It is concluded that because of the above attributes of statins, they are suitable candidates for reduction of post-PCI restenosis and post-PCI myonecrosis. The future directions for the use of statins in reduction of post-PCI restenosis and myonecrosis have been discussed.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".