Minimally Invasive Versus Open Liver Resections for Hepatocellular Carcinoma in Patients With Metabolic Syndrome
Bibliographic record
Abstract
OBJECTIVE: To compare minimally invasive (MILR) and open liver resections (OLRs) for hepatocellular carcinoma (HCC) in patients with metabolic syndrome (MS). BACKGROUND: Liver resections for HCC on MS are associated with high perioperative morbidity and mortality. No data on the minimally invasive approach in this setting exist. MATERIAL AND METHODS: A multicenter study involving 24 institutions was conducted. Propensity scores were calculated, and inverse probability weighting was used to weight comparisons. Short-term and long-term outcomes were investigated. RESULTS: A total of 996 patients were included: 580 in OLR and 416 in MILR. After weighing, groups were well matched. Blood loss was similar between groups (OLR 275.9±3.1 vs MILR 226±4.0, P =0.146). There were no significant differences in 90-day morbidity (38.9% vs 31.9% OLRs and MILRs, P =0.08) and mortality (2.4% vs 2.2% OLRs and MILRs, P =0.84). MILRs were associated with lower rates of major complications (9.3% vs 15.3%, P =0.015), posthepatectomy liver failure (0.6% vs 4.3%, P =0.008), and bile leaks (2.2% vs 6.4%, P =0.003); ascites was significantly lower at postoperative day 1 (2.7% vs 8.1%, P =0.002) and day 3 (3.1% vs 11.4%, P <0.001); hospital stay was significantly shorter (5.8±1.9 vs 7.5±1.7, P <0.001). There was no significant difference in overall survival and disease-free survival. CONCLUSIONS: MILR for HCC on MS is associated with equivalent perioperative and oncological outcomes to OLRs. Fewer major complications, posthepatectomy liver failures, ascites, and bile leaks can be obtained, with a shorter hospital stay. The combination of lower short-term severe morbidity and equivalent oncologic outcomes favor MILR for MS when feasible.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".