Evidence map›Paper›PMID 37277274›Full record

ArticleEuropean urology focus2023

A Multi-institution Study on the Association of Virtual Reality Skills with Continence Recovery after Robot-assisted Radical Prostatectomy.

Timothy N Chu, Elyssa Y Wong, Runzhuo Ma, Cherine H Yang, Istabraq S Dalieh, Alvin Hui, Oscar Gomez, Steven Cen, Ahmed Ghazi, Brian J Miles and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology focus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. 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

14 authors at 5 institutions in 1 country.

Timothy N ChuCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Elyssa Y WongCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Runzhuo MaCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Cherine H YangCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Istabraq S DaliehCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Alvin HuiCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Oscar GomezCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Steven CenDepartment of Radiology, University of Southern California, Los Angeles, CA, USA.
Ahmed GhaziDepartment of Urology, University of Rochester, Rochester, NY, USA.
Brian J MilesDepartment of Urology, Houston Methodist, Houston, TX, USA.
Clayton LauDepartment of Urology, City of Hope, Duarte, CA, USA.
John W DavisDepartment of Urology, MD Anderson Cancer Center, Houston, TX, USA.
Mitchell G GoldenbergCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Andrew J HungCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA. Electronic address: ajhung@gmail.com.
University of Southern California · USCity of Hope · USHouston Methodist · USThe University of Texas MD Anderson Cancer Center · USUniversity of Rochester · US

Funding

Automated Assessment for Robotic Suturing Utilizing Deep Learning AlgorithmsR01CA251579 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HUNG, ANDREW · 2021 to 2025
$3.0M
NCI NIH HHS R01 CA251579
6 · The paper itself

Abstract

backgroundVirtual reality (VR) simulators are increasingly being used for surgical skills training. It is unclear what skills are best improved via VR, translate to live surgical skills, and influence patient outcomes.

objectiveTo assess surgeons in VR and live surgery using a suturing assessment tool and evaluate the association between technical skills and a clinical outcome. DESIGN, SETTING, AND

participantsThis prospective five-center study enrolled participants who completed VR suturing exercises and provided live surgical video. Graders provided skill assessments using the validated End-To-End Assessment of Suturing Expertise (EASE) suturing evaluation tool. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A hierarchical Poisson model was used to compare skill scores among cohorts and evaluate the association of scores with clinical outcomes. Spearman's method was used to assess correlation between VR and live skills. RESULTS AND LIMITATIONS: Ten novices, ten surgeons with intermediate expertise (median 64 cases, interquartile range [IQR] 6-80), and 26 expert surgeons (median 850 cases, IQR 375-3000) participated in this study. Intermediate and expert surgeons were significantly more likely to have ideal scores in comparison to novices for the subskills needle hold angle, wrist rotation, and wrist rotation needle withdrawal (p < 0.01). For both intermediate and expert surgeons, there was positive correlation between VR and live skills for needle hold angle (p < 0.05). For expert surgeons, there was a positive association between ideal scores for VR needle hold angle and driving smoothness subskills and 3-mo continence recovery (p < 0.05). Limitations include the size of the intermediate surgeon sample and clinical data limited to expert surgeons.

conclusionsEASE can be used in VR to identify skills to improve for trainee surgeons. Technical skills that influence postoperative outcomes may be assessable in VR. PATIENT SUMMARY: This study provides insights into surgical skills that translate from virtual simulation to live surgery and that have an impact on urinary continence after robot-assisted removal of the prostate. We also highlight the usefulness of virtual reality in surgical education.

Indexed as

RoboticsVirtual RealityHumansMaleProspective StudiesProstateProstatectomyRoboticsSkills assessmentSurgical trainingVirtual reality

Identifiers

PMID37277274
PMCPMC10693649
OpenAlexW4379207433

What OpenQuestion holds

Textmetadata
LicenceTDM
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.