Pharmacological treatment for prevention of restenosis
Bibliographic record
Abstract
Coronary artery disease (CAD) is the leading cause of mortality and morbidity among adults in the Western world. Coronary artery bypass grafting and percutaneous coronary interventions (PCI) have gained widespread acceptance for the treatment of symptomatic CAD. There has been an explosive growth worldwide in the utilisation of PCI, such as balloon angioplasty and stenting, which now accounts for over 50% of coronary revascularisation. Despite the popularity of PCI, the problem of recurrent narrowing of the dilated artery (restenosis) continues to vex investigators. In recent years, significant advances have occurred in the understanding of restenosis. Two processes seem to contribute to restenosis: remodelling (vessel size changes) and intimal hyperplasia (vascular smooth muscle cell [VSMC] proliferation and extracellular matrix [ECM] deposition). Despite considerable efforts, pharmacological approaches to decrease restenosis have been largely unsuccessful and the only currently applied modality to reduce the restenosis rate is stenting. However, stenting only prevents remodelling and does not inhibit intimal hyperplasia. Several potential targets for inhibiting restenosis are currently under investigation including platelet activation, the coagulation cascade, VSMC proliferation and migration, and ECM synthesis. In addition, new approaches for local drug therapy, such as drug eluting stents, are currently being evaluated in preclinical and clinical studies. In this article, we critically review the current status of drugs that are being evaluated for restenosis at various stages of development (in vitro, preclinical animal models and human trials).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".