Bibliographic record
Abstract
A cardiovascular disease event in a transplant recipient may be the result of a pretransplantation disease process, a direct effect of immunosuppressant medications, or the result of exposure to a variety of traditional and nontraditional risk factors after transplantation. Although the understanding of posttransplantation cardiovascular disease remains incomplete, there is evidence that the impact of posttransplantation cardiovascular disease has been decreased, through increased attention to this problem. In the absence of controlled studies to guide therapy, this review summarizes treatment of cardiovascular disease risk factors for which there is strong evidence of benefit in the nontransplantation setting, observational evidence of a similar risk in transplant recipients, and evidence that treatment can be safely administered to transplant recipients. Putative risk factors for posttransplantation cardiovascular disease for which the current level of evidence is insufficient to support specific treatment recommendations are also discussed. Potential new strategies to decrease the risk for cardiovascular disease events after transplantation in the future, including aggressive pretransplantation risk reduction, individualized treatments to prevent different types of cardiovascular disease, dedicated efforts to reduce cardiovascular disease events during transitions between dialysis and transplantation, and manipulation of immunosuppressant protocols, are also introduced.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".