Evidence map›Paper›PMID 36349334›Full record

Trial reportContrast media & molecular imaging2022

Comparison of Single-Person Laparoscopic Appendectomy Using a Novel Brace-Assisted Camera Holding System and Conventional Laparoscopic Appendectomy: A Neural Network Algorithm Analysis.

Nengyun Zhang, Yuanhong Li, Renou Zhou

Open access · hybridAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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  5. Review
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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

3 authors at 1 institution in 1 country.

Nengyun ZhangDepartment of General Surgery, The Affliated People's Hospital of Ningbo University, Ningbo 315040, Zhejiang, China.ORCID 0000-0002-2224-823X
Yuanhong LiDepartment of General Surgery, The Affliated People's Hospital of Ningbo University, Ningbo 315040, Zhejiang, China.
Renou ZhouDepartment of General Surgery, The Affliated People's Hospital of Ningbo University, Ningbo 315040, Zhejiang, China.
Ningbo University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute appendicitis represents one of the main causes of surgical emergencies. It can be approached as an open appendectomy or a laparoscopic appendectomy (LA). However, LA generally requires the cooperation of a surgeon and an assistant. This study aims to compare the safety and efficacy of the novel brace-assisted single-person laparoscopic appendectomy (BASPLA) with conventional laparoscopic appendectomy (CLA) in the treatment of patients diagnosed with acute appendicitis by neural network algorithm analysis. Methods: Between January 2020 and December 2021,a total of 120 adult patients with acute appendicitis were randomized to the BASPLA group (62 cases) and the CLA group (58 cases).The clinical data were compared between the two groups, including demographics, clinical characteristics, and outcomes. Results: There was no significant difference in patients' pain scores before operation ( Conclusions: BASPLA for adult acute appendicitis can be a substitute for CLA, BASPLA is comparable to CLA in postoperative pain and quality of life. Compared to surgical assistants, it not only provides a stable, clear image for the surgeon but also frees up personnel. Especially in emergency surgery, it can achieve satisfactory clinical efficacy without requiring an assistant.

Indexed as

AppendicitisLaparoscopyAcute DiseaseAdultAppendectomyHumansNeural Networks, ComputerQuality of LifeTreatment Outcome

Identifiers

PMID36349334
PMCPMC9630036
OpenAlexW4296426051

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.