Risk factors of postoperative delirium after liver transplantation: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to summarize the incidence and risk factors of postoperative delirium (POD) after liver transplantation (LT) and associations of POD after LT with outcomes. EVIDENCE ACQUISITION: A literature search of Pubmed, EMBASE, and the Cochrane Databases was performed to identify studies reporting POD after LT. The Newcastle-Ottawa Scale was used to rate study quality. Effect estimates were extracted and combined using random-effect model. Pooled mean differences and odds ratios for individual risk factors were calculated using inverse-variance method and Mantel-Haenszel method, as appropriate. EVIDENCE SYNTHESIS: Eight articles with 1434 patients were included in the meta-analysis. Overall, the pooled estimated incidence rates of POD after LT were 30% (95% confidence interval: 20-39%). Fourteen statistically significant risk factors were identified in the pooled analysis: alcohol excess, preoperative renal replacement therapy (RRT), preoperative hospital length of stay (LOS), depression, hepatic encephalopathy, alcohol etiology of liver failure, Child-Turcotte-Pugh Score, APACHE II Score, MELD Score, preoperative INR, preoperative bilirubin, intraoperative use of fentanyl, intraoperative RBC transfusion, postoperative ammonia. Patients with POD had a significantly increased mechanical ventilation, postoperative RRT, LOS and mortality rate compared with those without POD. CONCLUSIONS: POD after LT was common and multifactorial in etiology. There are significant associations of POD after LT with some clinical outcomes. Effective interventions during perioperative period may be promising to reduce the risk of POD after LT.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".