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Record W4410417936 · doi:10.1186/s12893-025-02934-5

A comparison of postoperative outcomes between robotic-assisted and laparoscopic-assisted total gastrectomy: a comprehensive meta-analysis and systematic review

2025· review· en· W4410417936 on OpenAlexaboutno aff
Jianhua Chen, Fei Wang, Yong Wang, Jing Zhou, Yapeng Yang, Ziming Zhao, Rongfan Wu, Liuhua Wang, Jun Ren

Bibliographic record

VenueBMC Surgery · 2025
Typereview
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisSurgeryGastrectomyGeneral surgeryLaparoscopyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The application of robot-assisted technology in gastric cancer surgery is gradually gaining attention from surgeons. In this meta-analysis, our main objective was to assess whether robot-assisted techniques are more advantageous than laparoscopic-assisted technology in total gastrectomy. METHODS: We searched Pubmed, Embase, Web of Science, and Cochrane Library databases for clinical studies published before October 2023 comparing robotic-assisted total gastrectomy (RATG) and laparoscopic-assisted total gastrectomy (LATG) for gastric cancer. Non-clinical studies, data unavailability, or fewer than 50 included cases were excluded. The Newcastle-Ottawa Scale was used to assess the risk of bias by determining the quality of the observational studies. Statistical meta-analysis and drawing were performed using the Software Review Manager version 5.3 and Stata version 16.0. P < 0.05 was considered significant. RESULTS: Nine studies that included 1,864 patients with gastric cancer were included, published between 2012 and 2023. The results of the analysis showed that RATG has advantages in the following aspects: intraoperative blood loss was 17.69 ml lower in the RATG group than in the LATG group (WMD: -17.69,95% CI:-20.90 ∼ -14.49; P < 0.05); In terms of the number of resected lymph nodes, the RATG group had 2.65 more than the LATG group (WMD: 2.65,95% CI:0.88 ∼ -4.42); P < 0.05); the time to start liquid and postoperative hospital stays were 0.62 and 0.90 days shorter in the RATG group than in the LATG group, respectively (WMD: -0.62,95%CI: -1.06 ∼ -0.19; P < 0.05), (WMD: -0.90,95%CI: -1.43 ∼ -0.37; P < 0.05)); the incidence of major complications and pancreas fistula in the RATG group was 0.59% and 0.17% lower than in the LATG group, respectively (OR: 0.59,95% CI: 0.38 ∼ 0.93; P < 0.05), (OR: 0.17,95% CI: 0.03 ∼ 0.94; P < 0.05). However, the analysis showed that the operative time in the RATG group was 30.96 min longer than in the LATG group (WMD: 30.96,95% CI: 21.24 ∼ 40.69; P < 0.05). CONCLUSIONS: Based on the results of this meta-analysis, we concluded that robotic-assisted technology may be a worthwhile technique to apply in the surgical treatment of total gastrectomy. However, this meta-analysis has the limitations that the included studies were all non-randomized controlled trials and published in Asian countries, and more high-quality randomized controlled trials are needed for further validation in the future. THE REGISTERED NAME AND REGISTRATION NUMBER: The study protocol for this meta-analysis is registered on the PROSPERO website under registration number CRD42024500512.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.039
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.209
GPT teacher head0.419
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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