Modifiable risk factors of acute kidney injury after liver transplantation: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a common and critical complication of liver transplantation (LT), which is associated with increased morbidity, mortality and health care cost. We aimed to identify modifiable risk factors of AKI after LT. METHODS: A literature search of Pubmed, EMBASE and Cochrane Databases was performed to identify studies investigating risk factors of AKI after LT. The Newcastle-Ottawa Scale was used to rate study quality. Effect size and 95% confidence interval were pooled using a random-effect model with inverse-variance method. RESULTS: , ABO-incompatible LT, low graft to recipient body weight ratio, intraoperative hypotension, major bleeding, intraoperative use of vasopressor, large RBC transfusion, postreperfusion syndrome, postoperative use of vasopressors, overexposure to calcineurin inhibitor, calcineurin inhibitor without mycophenolate mofetil, graft dysfunction and infection. A total of 38 articles were included in the systematic review, in which 8 modifiable risk factors and 1 protective factor were additionally associated in single studies with the incidence of AKI after LT. CONCLUSIONS: Effective interventions based on identified modifiable risk factors in the perioperative management and graft allocation and preservation may be promising to reduce the incidence of AKI after LT. TRIAL REGISTRATION: The protocol for this systematic review is registered with PROSPERO (No. CRD42020166918 ).
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".