Cardiac allograft vasculopathy: pathology, prevention and treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiac transplantation is a recognized therapy for end-stage heart failure. Graft coronary artery disease is a chief determinant of long-term survival following cardiac transplantation. There are multiple purported etiologies for graft coronary artery disease including both immunologic and nonimmunologic factors. Immunologic factors include human leukocyte antigen mismatching, cytokine production, and activation of the cellular immune system. Nonimmunologic factors include diabetes, hypertension, hyperlipidemia, and cytomegalovirus infection, just to name a few. There are also donor and recipient factors including age, prior coronary artery disease in the donor heart, and mode of donor brain death. RECENT FINDINGS: The diagnosis of graft coronary artery disease is especially difficult, partially due to the de-innervated allograft, as well as to its inherent predilection to affect the medium-sized and smaller arteries in a concentric and diffuse nature. Conventional angiography can overlook this condition because of the lack of eccentric plaques in larger epicardial arteries. Intravascular ultrasonography, by contrast, is more sensitive in detecting graft coronary artery disease but is unable to visualize the entire arterial system. Treatment is challenging and often unrewarding, leading to re-transplantation. Prevention is therefore ideal and involves protection against endothelial injury before and during transplantation as well as after transplantation, with decreased ischemic time, aggressive attention to early rejection, risk factor modification, and close follow-up. SUMMARY: This review will look at the pathophysiology of graft coronary artery disease, current diagnostic and therapeutic choices, as well as existing and future directions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".