Angiogenesis: a Future Treatment Approach for Coronary Heart Disease
Bibliographic record
Abstract
Coronary artery disease is a leading cause of morbidity and mortality in Canada and the western world. Despite increased awareness, better management of risk factors, and improved non-surgical and surgical treatment modalities, coronary artery disease can sometimes involve the vessels of some patients so severely that medications, angioplasty and coronary artery bypass grafting may be unsuccessful at alleviating heart symptoms and preventing complications. However, patients may in the future benefit from a biologically-based therapy called therapeutic angiogenesis, which recreates the highly potent physiologic processes that occur during growth and development in every animal and human being, with the goal of forming new blood vessels in the adult heart. What is angiogenesis? Angiogenesis is the formation of new blood vessels from preexisting ones. This process occurs somewhat naturally in the heart of patients who progressively develop coronary disease over a number of months to years, yet to a degree that is usually insufficient to completely alleviate cardiac symptoms and prevent subsequent complications. The sequence of events leading to angiogenesis is depicted in Figure 1. Angiogenesis is a very complex process, and it is believed that the actions of growth factors and of a locally produced gas called nitric oxide interplay to detach, multiply, rearrange, and recruit cells in order to create
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".