Robotic Versus Laparoscopic Gastrectomy for Gastric Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background To date, robotic surgery has been widely used worldwide. We conducted a systematic review and meta-analysis to evaluate short- and long-term outcomes of robotic gastrectomy (RG) in gastric cancer patients to determine whether RG can replace laparoscopic gastrectomy (LG).Methods Pubmed, Cochrane Library, WanFang, China National Knowledge Infrastructure (CNKI) and VIP databases were comprehensively searched for studies published before May 2020 that compared RG with LG. Next, two independent reviewers conducted literature screening and data extraction. The quality of the literature was assessed using the Newcastle-Ottawa Scale (NOS),and the data analyzed using the Review Manager 5.3 software. Random effects or fixed effects models were applied according to heterogeneity.Results A total of 19 studies including 7275 patients were included in the meta-analyses, of which 4598 patients were in the LG group and 2677 in the RG group. Compared with LG,RG was associated with longer operative time (WMD=−32.96 min; 95% CI:-42.08~-23.84, P<0.00001),less blood loss (WMD=28.66 ml; 95% CI: 18.59~38.73, P<0.00001),and shorter time to first flatus (WMD=0.16days; 95%CI:0.06~0.27, P=0.003).There was no significant difference between RG and LG in terms of the hospital stay (WMD=0.23days, 95 % CI:-0.53~0.98, P=0.56),overall postoperative complication (OR=1.07, 95 % CI:0.91~1.25, P=0.43),mortality (OR=0.67, 95% CI=0.24~1.90, P=0.45),the number of harvested lymph nodes (WMD=-0.96, 95% CI:-2.12~0.20, P=0.10),proximal resection margin (WMD=-0.10 cm,95% CI:-0.29~0.09, P=0.30),and distal resection margin (WMD=0.15cm,95% CI:-0.21~0.52, P=0.41).No significant differences were found between the two treatments in overall survival(OS) (HR=0.95, 95% CI:0.76~1.18; P=0.64), recurrence-free survival(RFS) (HR=0.91, 95% CI:0.69~1.21;P=0.53), and recurrence rate (OR=0.90, 95% CI:0.67~1.21; P=0.50). Conclusions The results of this study suggested that RG is as acceptable as LG in terms of short-term and long-term outcomes. RG can be performed as effectively and safely as LG. Moreover, more randomized controlled trials comparing the two techniques with rigorous study designs are still essential to evaluate the value of the robotic surgery for gastric cancer.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| 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 | 0.002 |
| 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".