Risk factors of invasive fungal infections in liver transplant recipients: A systematic review and meta-analysis
Bibliographic record
Abstract
Invasive fungal infections (IFIs) remain one of the most common infectious complications after organ transplantation, and liver transplant recipients (LTRs) have the highest mortality rate. However, risk factors associated with IFIs have only been evaluated in small single-center studies. We performed a meta-analysis by conducting a comprehensive search using Ovid MEDLINE, Ovid Embase, Cochrane database of systematic reviews, and Cochrane central register of controlled trials. All case-control and cohort studies evaluating risk factors for IFIs in adult LTRs were screened. Utilizing a random-effects model, a multivariate analysis was completed, and 28 studies were eligible for meta-analysis. Rates of IFIs ranged from 1.4% to 32.7%. Previous antibiotic use (OR 9.3; 95% CI 3.2-27.0) and bacterial infection (OR 4.3; 95% CI 2.1-8.6) were risk factors of invasive candidiasis. Yet for invasive aspergillosis, posttransplant renal replacement therapy (OR 9.2; 95% CI 4.2-20.4), reoperation (OR 8.0; 95% CI 2.9-21.7), and cytomegalovirus infection (OR 6.2; 95% CI 2.0-19.3) were risk factors. The top independent risk factors for IFIs during studies from 2010 to 2019 were previous fungal colonization (OR 9.19; 95% CI 4.92-17.16), reoperation (OR 5.45; 95% CI 2.93-10.15), and previous bacterial infections (OR 3.81; 95% CI 2.13-6.83). These risk factors may be targeted by antifungal prophylaxis in LTRs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".