Mechanisms, Consequences, and Prevention of Coronary Graft Failure
Bibliographic record
Abstract
Graft failure occurs in a sizeable proportion of coronary artery bypass conduits. We herein review relevant current evidence to give an overview of the incidence, pathophysiology, and clinical consequences of this multifactorial phenomenon. Thrombosis, endothelial dysfunction, vasospasm, and oxidative stress are different mechanisms associated with graft failure. Intrinsic morphological and functional features of the bypass conduits play a role in determining failure. Similarly, characteristics of the target coronary vessel, such as the severity of stenosis, the diameter, the extent of atherosclerotic burden, and previous endovascular interventions, are important determinants of graft outcome and must be taken into consideration at the time of surgery. Technical factors, such as the method used to harvest the conduits, the vasodilatory protocol, the storage solution, and the anastomotic technique, also play a major role in determining graft success. Furthermore, systemic atherosclerotic risk factors, such as age, sex, diabetes mellitus, hypertension, and dyslipidemia, have been variably associated with graft failure. The failure of a coronary graft is not always correlated with adverse clinical events, which vary according to the type, location, and reason for failed graft. Intraoperative flow verification and secondary prevention using antiplatelet and lipid-lowering agents can help reducing the incidence of graft failure.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".