Evidence map›Paper›PMID 36204945›Full record

ArticleJournal of minimal access surgery

Robotic real-time vessel navigation using indocyanine green fluorescence for lymph node dissection along the left gastroepiploic vessels during robotic distal gastrectomy - First experience.

Yuma Ebihara, Yo Kurashima, Soichi Murakami, Toshiaki Shichinohe, Satoshi Hirano

Open access · goldAbstract read
In one paragraph

Article in Journal of minimal access surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Yuma EbiharaDepartment of Gastroenterological Surgery II, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Yo KurashimaDepartment of Gastroenterological Surgery II, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Soichi MurakamiDepartment of Gastroenterological Surgery II, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Toshiaki ShichinoheDepartment of Gastroenterological Surgery II, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Satoshi HiranoDepartment of Gastroenterological Surgery II, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Hokkaido University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Splenic infarction (SI) following gastrectomy is common; severe complications related to SI, such as splenic haemorrhage, abscess formation or rupture, can be fatal. To overcome these problems, we performed real-time vessel navigation using indocyanine green (ICG) fluorescence during robotic distal gastrectomy (RDG). The aim of study is to report the efficacy of robotic real-time vessel navigation for lymph node dissection (LND) along left gastroepiploic vessels (LGEVs). We treated seven patients with gastric cancer who underwent LND along the LGEVs using robotic real-time vessel navigation during RDG at our institution from January 2021 to July 2021. There were no complications (Clavien-Dindo classification II). There were no cases of post-operative SI or spleen-related complications. Robotic real-time vessel navigation using ICG for LND along LGEVs during RDG could help to reduce post-operative spleen-related complications associated with RDG.

Indexed as

Left gastroepiploic vesselspost-operative complicationsrobotic distal gastrectomysplenic infarction

Identifiers

PMID36204945
PMCPMC9632694
OpenAlexW4282918927

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.