Evidence map›Paper›PMID 36504515›Full record

ReviewWorld journal of gastrointestinal surgery2022

Development and future perspectives of natural orifice specimen extraction surgery for gastric cancer.

Zhi-Cao Zhang, Qi-Fa Luo, Wen-Sheng Wang, Jiang-Hong Chen, Chen-Yu Wang, Dan Ma

Abstract readReview
In one paragraph

Review in World journal of gastrointestinal surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. [Clinical Study of Robotic Natural Orifice Specimen Extraction Surgery for Right Colon Cancer].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026
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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

6 authors.

Zhi-Cao ZhangDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China.
Qi-Fa LuoDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China.
Wen-Sheng WangDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China.
Jiang-Hong ChenDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China.
Chen-Yu WangDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China.
Dan MaDepartment of General Surgery, Xinqiao Hospital of Army Medical University, Chongqing 400037, China. 1054727918@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, natural orifice specimen extraction surgery (NOSES), a novel minimally invasive surgical technique, has become a focus in the surgical field, and has been initially applied in gastric surgery in many national medical centers worldwide. In addition, this new surgical technique was launched in major hospitals in China. With an increasing number of patients who have accepted this new surgical technique, NOSES has provided new prospects for the treatment of gastric cancer (GC), which may achieve a better outcome for both patients and surgeons. More and more experts and scholars from different countries and regions are currently paying close attention to NOSES for the treatment of GC. However, there are only a few reports of its use in GC. This review focuses on the research progress in NOSES for radical gastrectomy in recent years. We also discuss the challenges and prospects of NOSES in clinical practice.

Indexed as

GastrectomyGastric cancerLaparoscopic surgeryMinimally invasive surgeryNatural orifice specimen extraction surgeryRadical gastrectomy

Identifiers

PMID36504515
PMCPMC9727573

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Registered trials

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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.