Risk Factors for Bacterial Infection After Liver Transplant: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
OBJECTIVES: Bacterial infection is an important cause of early death after liver transplant. This meta-analysis aimed to determine the risk factors for bacterial infection after liver transplant. MATERIALS AND METHODS: We searched for relevant studies published in PubMed,Web of Science, Embase, The Cochrane Library, China National Knowledge Infrastructure, Wan Fang Database, Chinese Science and Technology Journal Database, and China Biomedical Literature Database up to May 2024. After literature screening, we used the Newcastle-Ottawa Scale to evaluate the quality of included studies. The fixed-effect or random-effect model was used to calculate the combined odds ratio and corresponding 95% CI. We used the I 2 test to evaluate whether there was heterogeneity among studies. RESULTS: The 23 included articles reported on 6426 adult liver transplant patients and 1427 cases of bacterial infection. Preoperative hepatic encephalopathy (odds ratio = 2.55; 95% CI, 1.48-4.41), Model for End-Stage Liver Disease score (odds ratio=2.09; 95% CI, 1.10-3.97), Child-Pugh C score (odds ratio = 4.87; 95% CI, 3.22-7.37), hypoproteinemia (odds ratio = 2.88; 95% CI, 1.84-4.50), use of antibiotics (odds ratio = 3.62; 95% CI, 1.83-7.17), intraoperative blood transfusion (odds ratio = 2.14; 95% CI, 1.04-4.38), intraoperative bleeding (odds ratio = 2.74; 95% CI, 1.53-4.90), ventilator time (odds ratio = 3.24; 95% CI, 1.88-5.57), stay in intensive care unit (odds ratio = 5.17; 95% CI, 3.35-7.99), and hospitalization time (odds ratio = 1.03; 95% CI, 3.06-7.61) were influencing factors but not age. CONCLUSIONS: Further strict and well-designed studies with sufficient sample size are needed to identify risk factors to address the limitations of our study. Strengthening the assessment and screening of risk factors and effective intervention as soon as possible are conducive to improving the clinical outcomes of liver transplant recipients.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".