The incidence and risk factors of acute lung injury after liver transplantation: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: End-stage liver disease is associated with significant global morbidity, with liver transplantation being the only curative treatment option. Posttransplant acute lung injury (ALI) and acute respiratory distress syndrome can adversely affect outcomes. This study aimed to evaluate the incidence of ALI following liver transplantation and to identify associated risk factors. METHODS: A comprehensive literature search was conducted up to February 24, 2024, across databases such as PubMed, EMBASE, Cochrane Controlled Register of Trials, and Web of Science. Studies were included if they reported the incidence of ALI in liver transplant patients. The quality of the studies was assessed using the Newcastle-Ottawa scale. Data analysis utilized fixed-effects and random-effects models based on heterogeneity, and subgroup analyses investigated the impact of age, region, and study design on ALI incidence. Publication bias was evaluated through funnel plots and Egger's test. RESULTS: The meta-analysis comprised 11 studies from 2000 to 2023, assessing 10,007 liver transplant patients, among whom 198 cases of ALI were reported. Incidence rates varied significantly from 0.1% to 44.6%. The pooled incidence rate was 0.14 (95% confidence interval: 0.06; 0.25), indicative of high heterogeneity (I2 = 97%). Subgroup analyses revealed higher incidence rates in Asian studies and pediatric populations, while retrospective studies reported a lower incidence compared to prospective ones. Publication bias was confirmed. CONCLUSION: The study found a 14% incidence of lung injury post-liver transplantation, with variation by age and region, underscoring the need for personalized perioperative care and targeted monitoring for high-risk patients.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".