Impact of Sarcopenic Obesity on Outcomes in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To evaluate preoperative body composition, including skeletal muscle and visceral adipose tissue, and to clarify the impact on outcomes after hepatectomy for hepatocellular carcinoma (HCC). BACKGROUND: Recent studies have indicated that sarcopenia is associated with morbidity and mortality in various pathologies, including cancer, and that obesity or visceral adiposity represents a significant risk factor for several cancers. However, the impact of sarcopenic obesity on outcomes after hepatectomy for HCC has not been fully investigated. METHODS: We retrospectively analyzed 465 patients who underwent primary hepatectomy for HCC between April 2005 and March 2015. Skeletal muscle mass and visceral adipose tissue were evaluated by preoperative computed tomography to define sarcopenia and obesity. Patients were classified into 1 of 4 body composition groups according to the presence or absence of sarcopenia and obesity. RESULTS: Body composition was classified as nonsarcopenic nonobesity in 184 patients (39%), nonsarcopenic obesity in 219 (47%), sarcopenic nonobesity in 31 (7%), and sarcopenic obesity in 31 (7%). Compared with patients with nonsarcopenic nonobesity, patients with sarcopenic obesity displayed worse median survival (84.7 vs. 39.1 mo, P = 0.002) and worse median recurrence-free survival (21.4 vs. 8.4 mo, P = 0.003). Multivariate analysis identified sarcopenic obesity as a significant risk factor for death (hazard ratio [HR] = 2.504, P = 0.005) and HCC recurrence (HR = 2.031, P = 0.006) after hepatectomy for HCC. CONCLUSION: Preoperative sarcopenic obesity was an independent risk factor for death and HCC recurrence after hepatectomy for HCC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".