Cardiovascular events after liver transplantation: MACE hurts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it