Influence of body fat tissue on outcomes in patients undergoing hepatectomy or liver transplantation: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to investigate potential associations between body fat composition and postoperative outcomes in patients with hepatectomy or liver transplantation. METHODS: Three online databases, including Embase, PubMed, and the Cochrane Library, were thoroughly searched for literature describing the relationship between body fat composition and outcomes of patients with liver surgery from the start of each database to 29 October 2023. The Newcastle-Ottawa Scale was used to rate the quality of the studies. RESULTS: This analysis included a total of 29 articles with a combined patient cohort of 6435 individuals. The results demonstrated that patients with high intramuscular fat content (IMFC) had significantly inferior overall survival (OS) [hazard ratio (HR): 2.07, 95% CI: 1.69-2.53, P <0.001] and recurrence-free survival (RFS) (HR: 1.61, 95% CI: 1.20-2.16, P =0.002) and a higher risk of major complications (HR: 2.20, 95% CI: 1.59-3.05, P <0.001). We also found that the presence of high visceral-to-subcutaneous fat tissue ratio (VSR) in patients with liver surgery was significantly related to poorer OS (HR: 1.70, 95% CI: 1.44-2.00, P <0.001) and progression-free survival (PFS) (HR: 1.29, 95% CI: 1.11-1.50, P =0.001) and a higher major complication rate (HR: 2.31, 95% CI: 1.17-4.56, P =0.016). Besides, the synthesized findings indicated there is no significant correlation between visceral fat tissue and survival outcomes or postoperative complications. CONCLUSION: In summary, preoperative IMFC and VSR have the potential to forecast poorer OS and RFS and a higher risk of complications for patients undergoing hepatectomy or liver transplantation.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".