Renal Dysfunction in Liver Transplantation: The Problem and Preventive Strategies
Bibliographic record
Abstract
Ongoing improvements in survival following liver transplantation have necessitated a re-evaluation of immunosuppression protocols. Corticosteroids and calcineurin inhibitors (CNIs) are the most frequently used immunosuppressive drugs for liver transplantation but are associated with a wide range of adverse effects, such as hypertension, hyperlipidemia and nephrotoxicity. The need for hemodialysis after liver transplantation is associated with poor outcomes. Renal dysfunction in this setting may be caused by pre-existing renal disease, hepatorenal syndrome and/or post-transplant factors, including the use of nephrotoxic drugs, most notably CNIs such as cyclosporine and tacrolimus. The methods that address this problem include the diligent control of metabolic factors (eg, hypertension and hyperlipidemia), therapeutic monitoring of CNIs and withdrawal or reduction of the dosage of CNIs, combined with the use of newer non-nephrotoxic agents. Although there is no clear consensus about the most effective strategy, the optimal long-term immunosuppressive regimen would prevent rejection without causing nephrotoxicity or other significant adverse effects. Recent evidence suggests that the liver is a tolerogenic organ and that some patients may need little, if any, long-term immunosuppression.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".