Bibliographic record
Abstract
Despite continued advances in the prevention and treatment of coronary artery disease, there are still a large number of patients who are not candidates for the conventional revascularization techniques of balloon angioplasty and stenting, or coronary artery bypass grafting (CABG). Therapeutic angiogenesis, in the form of the administration of growth factor protein or gene therapy, has emerged as a promising new method of treatment for patients with coronary artery disease. The goal of this strategy is to promote the development of supplemental blood conduits that will act as endogenous bypass vessels. New vessel formation occurs through the processes of angiogenesis, vasculogenesis, and arteriogenesis, under the control of growth factors such as those that belong to the vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and angiopoeitin (Ang) families of molecules. Preclinical studies have suggested that such an approach is both feasible and effective; however many questions remain to be answered. This review will address the elements of pharmacologic revascularization, focusing on gene and protein-based therapy. The important growth factors, the vector (for gene therapy), routes of delivery, the desired therapeutic effect, and quantifiable clinical end points for trials of angiogenesis will all be addressed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".