Cardiovascular events after liver transplantation: MACE hurts
Bibliographic record
Abstract
The curative therapy for patients with end-stage liver disease is liver transplantation. However, liver transplantation challenges the cardiovascular system, and is associated with major adverse cardiovascular events (MACE). Immediately after implantation of the liver graft, changes in cardiac preload and afterload increase the cardiac workload. Longer-term postoperatively, a more sedentary lifestyle and enhanced appetite increase obesity and body mass index. Immunosuppressants may also affect the cardiovascular system. All these factors that liver recipients encounter impact the function of the cardiovascular system. Cardiac events are the third-leading cause of death in liver recipients. This review describes the pertinent factors that predispose to development of MACE after liver transplantation, and how to predict these cardiovascular events in the post-transplant period. We review the roles of metabolic syndrome, renal dysfunction, non-alcoholic fatty liver disease, diagnostic tests such as imaging and biomarkers, and parameters such as systolic and diastolic dysfunction, and QT interval prolongation in cardiovascular events. We summarize the current literature on scoring systems to predict cardiovascular events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".