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Record W4386818322 · doi:10.1097/js9.0000000000000750

Comparison of safety and effectiveness between robotic and laparoscopic major hepatectomy: a systematic review and meta-analysis

2023· review· en· W4386818322 on OpenAlexaboutno aff
Benliang Mao, Shanfei Zhu, Dan Li, Junhao Xiao, Bailin Wang, Yong Yan

Bibliographic record

VenueInternational Journal of Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
FundersBasic and Applied Basic Research Foundation of Guangdong Province
KeywordsMedicineMeta-analysisHepatectomyGeneral surgerySurgeryInternal medicineResection

Abstract

fetched live from OpenAlex

BACKGROUND: Robotic platform has been increasingly applied in major hepatectomy. However, the role or advantage of robotic approach comparing with laparoscopic approach in major hepatectomy remains controversial. This meta-analysis compares perioperative outcomes of robotic major hepatectomy (RMH) to laparoscopic major hepatectomy (LMH) for hepatic neoplasms. METHODS: PubMed, Web of Science, EMBASE, and Cochrane Library were searched to identify comparative studies compared RMH versus LMH for hepatic neoplasms. The search timeframe was set before May 2023. Main outcomes were mortality, overall morbidities, serious complications, and conversion to open surgery. Secondary outcomes were operative time, intraoperative blood loss, blood transfusion, postoperative length of hospital stay, R0 resection, reoperation, and readmission. Studies were evaluated for quality by Cochrane risk of bias tool or Newcastle-Ottawa scale. Data were pooled as odds ratio (OR) or mean difference (MD). This study was registered at PROSPERO (CRD42023410951). RESULTS: Twelve retrospective cohort studies concerning total 1657 patients (796 RMH, 861 LMH) were included. Meta-analyses showed no significant differences in mortality (OR=1.23, 95% CI=0.50-2.98, P =0.65), overall postoperative complications (OR=0.83, 95% CI=0.65-1.06, P =0.14), operative time (MD=6.47, 95% CI=-14.72 to 27.65, P =0.55), blood transfusion (OR=0.77, 95% CI=0.55-1.08, P =0.13), R0 resection (OR=1.45, 95% CI=0.91-2.31, P =0.12), reoperation (OR=0.76, 95% CI=0.31-1.88, P =0.56), and readmission (OR=0.63, 95% CI=0.28-1.44, P =0.27) between RMH and LMH. Incidence of serious complications (OR=0.60, 95% CI=0.40-0.90, P =0.01), conversion to open surgery (OR=0.41, 95% CI=0.27-0.63, P <0.0001), blood loss (MD=-91.42, 95% CI=-142.18 to -40.66, P =0.0004), and postoperative hospital stay (MD=-0.64, 95% CI=-0.78 to -0.49, P <0.00001) were reduced for RMH versus LMH. CONCLUSIONS: RMH is associated with comparable short-term surgical outcomes and oncologic adequacy compared to LMH when performed by experienced surgeons at large centres. RMH may result in reduced major morbidities, conversion rate, blood loss, and hospital stay, but these results were volatile. Further randomized studies should address the potential advantages of RMH over LMH.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.589
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0120.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.388
GPT teacher head0.422
Teacher spread0.034 · 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 teacher head, 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

Citations32
Published2023
Admission routes1
Has abstractyes

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