Thermal ablation versus liver resection for hepatocellular carcinoma in patients with cirrhosis: a systematic review and meta-analysis of propensity-score matched studies
Bibliographic record
Abstract
The outcomes of cirrhotic patients with hepatocellular carcinoma (HCC) after thermal ablation (TA) versus liver resection (LR) are debated. We aimed to compare the overall survival (OS), disease-free survival (DFS), and operative outcomes after TA and LR for HCC in patients with cirrhosis. Until November 15, 2022, we searched PubMed, Embase, and Cochrane databases by using Medical Subject Heading terms and other terms, and used the Newcastle-Ottawa literature evaluation scale to assess the quality of selected studies. OS, DFS, and operative outcomes were extracted and analyzed. The meta-analysis showed that 5 propensity-score matched (PSM) studies including 933 patients (463 TA vs. 470 LR) were included. After analysis, TA and LR had similar results at 1-year OS (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.01-2.78; P = 0.05) and 3-year OS (OR 0.76; 95% CI 0.56-1.04; P = 0.08), whereas LR increased 5-years OS (OR 0.37; 95% CI 0.18-0.74; P = 0.005). In addition to the DFS, the 1-year DFS was significantly higher in patients with LR. However, there were no obvious differences in 3-year and 5-year DFS when comparing TA and LR. The length of operative time and hospital stay were longer in the LR group. Besides, the LR group had significantly higher rate of perioperative blood transfusions and major complications. Our research proved that LR took advantage of OS and DFS for HCC patients with cirrhosis. Additional well-designed randomized controlled trials are needed.
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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) | 0.014 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".