A Systematic Review of Kidney Disease Pre- and Post-liver Transplantation
Bibliographic record
Abstract
Abstract Export Introduction: The main aim was to do a systematic review on the published studies to determine kidney disease in pre- and post-liver transplant and its determinants of liver transplantation. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (2020) guidelines were followed to conduct this study. All procedures followed the recommendations found in the Cochrane handbook. Full-text studies, acute kidney injury, and chronic kidney disease (CKD) pre- and post-liver transplantation were included in this study. We included papers from PsycINFO, Google Scholar, PubMed, Web of Science, Medline, Embase, Google, and the Cochrane Library in order to identify additional articles, even though the review ultimately covered all research. Results: A total of 884 references were removed because they were duplicates. The preliminary screening removed 201 of the 243 citations from consideration. There were 37 full-text articles considered for inclusion, but only 9 met the criteria; all the nine articles were very high quality. Totally 1846 patients were studied in this review from all the nine articles with an average age of 51.32 years. All articles were single-center prospective and retrospective observational studies. Conclusion: Kidney failure following liver transplantation is a prevalent and life-threatening complication. Complex events before and after LT cause the condition. The Model for End-Stage Liver Disease score-based organ allocation change is largely responsible for its rapid growth. The majority of studies indicated kidney function declined over time. This highlights the importance of monitoring liver transplant patients’ kidney function and CKD symptoms. Delaying or decreasing tacrolimus administration may help kidney functions.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".