Post–Liver Transplant Acute Kidney Injury
Bibliographic record
Abstract
Acute kidney injury (AKI) is a common condition following liver transplantation (LT). It negatively impacts patient outcomes by increasing the chances of developing chronic kidney disease and reducing graft and patient survival rates. Multiple definitions of AKI have been proposed and used throughout the years, with the International Club of Ascites definition being the most widely now used for patients with cirrhosis. Multiple factors are associated with the development of post-LT AKI and can be categorized into pre-LT comorbidities, donor and recipient characteristics, operative factors, and post-LT factors. Many of these factors can be optimized in an attempt to minimize the risk of AKI occurring and to improve renal function if AKI is already present. A special consideration during the post-LT phase is needed for immunosuppression as certain immunosuppressive medications can be nephrotoxic. The calcineurin inhibitor tacrolimus (TAC) is the mainstay of immunosuppression but can result in AKI. Several strategies including use of the monoclonoal antibody basilixamab to allow for delayed initiation of tacrolimus therapy and minimization through combination and minimization or elimination of TAC through combination with mycophenolate mofetil or mammalian target of rapamycin inhibitors have been implemented to reverse and avoid AKI in the post-LT setting. Renal replacement therapy may ultimately be required to support patients until recovery of AKI after LT. Overall, by improving renal function in post-LT patients with AKI, outcomes can be improved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".