Understanding and managing cardiovascular outcomes in liver transplant recipients
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
PURPOSE OF REVIEW: Cardiovascular disease (CVD) is a common cause of mortality after liver transplantation. The transplant community is focused on improving long-term survival. Understanding the prevalence of CVD in liver transplant recipients, precipitating factors as well as prevention and management strategies is essential to achieving this goal. RECENT FINDINGS: CVD is the leading cause of death within the first year after transplant. Arrhythmia and heart failure are the most often cardiovascular morbidities in the first year after transplant which could be related to pretransplant diastolic dysfunction. Pretransplant diastolic dysfunction is reflective of presence of cirrhotic cardiomyopathy which is not as harmless as it was thought. Multiple cardiovascular risk prediction models have become available to aid management in liver transplant recipients. SUMMARY: A comprehensive prevention and treatment strategy is critical to minimize cardiovascular morbidity and mortality after liver transplant. Weight management and metabolic syndrome control are cornerstones to any prevention and management strategy. Bariatric surgery is an underutilized tool in liver transplant recipients. Awareness of 'metabolic-friendly' immunosuppressive regimens should be sought. Strict adherence to the cardiology and endocrine society guidelines with regard to managing metabolic derangements post liver transplantation is instrumental for CVD prevention until transplant specific recommendations can be made.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 it