Risk Factors of Invasive Fungal Infection in Recipients After Liver Transplantation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: Invasive fungal infection (IFI) remains an important cause of mortality in liver transplantation (LT). The objective of this meta-analysis was to identify the risk factors for IFI after LT. Methods: We searched for relevant studies published up to June 2020 from PubMed, Web of Science, Embase, and the Cochrane Library. Odds ratios (ORs) and their corresponding 95% CIs were used to identify significant differences in the risk factors. Heterogeneity between studies was evaluated by the I2 test, and potential publication bias was assessed with Egger's test. The quality of included studies was evaluated with the Newcastle-Ottawa Scale (NOS). Results: A total of 14 studies enrolling 4,284 recipients were included in the meta-analysis. Reoperation (OR = 2.18, 95% CI: 1.61–2.94), posttransplantation dialysis (OR = 2.03, 95% CI: 1.52–2.72), bacterial infection (OR = 1.81, 95% CI: 1.33–2.46), live donor (OR = 1.78, 95% CI: 1.20–2.63), retransplantation (OR = 2.45, 95% CI: 1.54–3.89), and fungal colonization (OR = 2.60, 95% CI: 1.99–3.42) were associated with the risk factors of IFI after LT. Conclusions: Despite some risk factors that have been identified as significant factors for IFI post-LT, which may inform prevention recommendations, rigorous and well-designed studies with adequate sample sizes should be conducted to solve the limitations of this study.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".