Laparoscopic versus open repeat hepatectomy for recurrent hepatocellular carcinoma: a systematic review and meta-analysis of propensity score-matched cohort studies
Bibliographic record
Abstract
OBJECTIVE: The effectiveness of laparoscopic repeat hepatectomy (LRH) versus open repeat hepatectomy (ORH) on recurrent hepatocellular carcinoma (RHCC) is unclear. We compared the surgical and oncological outcomes of LRH and ORH in patients with RHCC with a meta-analysis of studies based on propensity score-matched cohorts. METHODS: A literature search was conducted on PubMed, Embase, and Cochrane Library with Medical Subject Headings terms and keywords until 30 September 2022. The quality of eligible studies was evaluated with the Newcastle-Ottawa Scale. Mean difference (MD) with a 95% CI was used for the analysis of continuous variables; odds ratio (OR) with 95% CI was used for binary variables; and hazard ratio with 95% CI was used for survival analysis. A random-effects model was used for meta-analysis. RESULTS: Five high-quality retrospective studies with 818 patients were included; 409 patients (50%) were treated with LRH and 409 (50%) with ORH. In most surgical outcomes, LRH was superior to ORH: less estimated blood loss, shorter operation time, lower major complication rate, and shorter length of hospital stay (MD=-225.9, 95% CI=[-360.8 to -91.06], P =0.001; MD=66.2, 95% CI=[5.28-127.1], P =0.03; OR=0.18, 95% CI=[0.05-0.57], P =0.004; MD=-6.22, 95% CI=[-9.78 to -2.67], P =0.0006). There were no significant differences in the remaining surgical outcomes: blood transfusion rate and overall complication rate. In oncological outcomes, LRH and ORH were not significantly different in 1-year, 3-year, and 5-year overall survival and disease-free survival. CONCLUSIONS: For patients with RHCC, most surgical outcomes with LRH were superior to those of ORH, but oncological outcomes with the two operations were similar. LRH may be a preferable option for the treatment of RHCC.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".