TITLE: Implantation of Drug Eluting Stents during Percutaneous Coronary Interventions: Review of Revascularization Rates due to In-Stent Restenosis
Bibliographic record
Abstract
Coronary artery disease (CAD) is a leading cause of morbidity and mortality throughout the world. 1 The underlying cause of CAD is the deposition of atherosclerotic plaques in the coronary arteries, which leads to stenosis, reduced blood flow, and ischemia. 2 Revascularization procedures such as coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) are used to correct the narrowing of the coronary blood vessels and restore blood flow to the ischemic areas. 2 During PCI, a catheter is threaded from an artery in the groin and advanced through the artery to the area of stenosis. Expandable bare metal stents (BMS) and drug eluting stents (DES) may then be placed to open the vessel or maintain vessel patency following PCI. 3 PCI can also be performed without stenting, for example in areas where a stent cannot be placed. 3 Drug eluting stents are metal stents that are covered with a polymer and an immunosuppressant or cytotoxic drug. 4 The drugs are incorporated to inhibit cell proliferation within the stent which may contribute to restenosis, a complication which was observed with BMS and prompted the development of DES. 4 There are a number of DES available in Canada
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".