Evidence map›Paper›PMID 40906316›Full record

ArticleJournal of robotic surgery2025

Technical performance assessment of robotic surgery: a systematic scoping review.

Seyed Mohammad Kalantar Motamedi, Sahar Fatima, Qian Zhang, Margaret J Foster, Jacob M Kolman, Raaghav Bageshwar, James L Lilly, Peter K Lin, Adriana Lopez, Stephen L Jones and 5 more

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Seyed Mohammad Kalantar MotamediDepartment of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Sahar FatimaDepartment of Anesthesiology and Critical Care, Houston Methodist, 6565 Fannin St, B452, Houston, TX, 77030, USA.
Qian ZhangCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, USA.
Margaret J FosterDepartment of Medical Education, School of Medicine, and Center for Systematic Reviews and Research Syntheses, University Libraries, Texas A&M University, College Station, TX, USA.
Jacob M KolmanAcademic Affairs, Houston Methodist, Houston, TX, USA.
Raaghav BageshwarSchool of Engineering Medicine (EnMed), Texas A&M University College of Medicine, Texas A&M College of Engineering and Houston Methodist Hospital, Houston, TX, USA.
James L LillySchool of Engineering Medicine (EnMed), Texas A&M University College of Medicine, Texas A&M College of Engineering and Houston Methodist Hospital, Houston, TX, USA.
Peter K LinSchool of Engineering Medicine (EnMed), Texas A&M University College of Medicine, Texas A&M College of Engineering and Houston Methodist Hospital, Houston, TX, USA.
Adriana LopezCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, USA.
Stephen L JonesCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, USA.
Gyusung I LeeDivision of Education, American College of Surgeons, Chicago, IL, USA.
Ganesh SankaranarayananDepartment of Surgery, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
Farzan SasangoharDepartment of Anesthesiology and Critical Care, Houston Methodist, 6565 Fannin St, B452, Houston, TX, 77030, USA.
Dimitrios StefanidisDepartment of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
Randolph H SteadmanDepartment of Anesthesiology and Critical Care, Houston Methodist, 6565 Fannin St, B452, Houston, TX, 77030, USA. rsteadman@houstonmethodist.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Defining performance errors in robotic surgery is critical for the assessment of robotic surgery skill. Our goal was to identify and categorize explicitly defined intraoperative technical errors in robotic surgery, how skill assessment was performed, and how ratings were conducted either manually by experts or via automated ratings. This scoping review included studies involving general, urologic, obstetrics/gynecologic, and thoracic surgery, and general skills as practiced in inanimate, virtual reality, in vivo/ex vivo animal, cadaver, and human operations. Primary empirical and consensus-building studies were included if they addressed intraoperative performance assessment or error definition and identification. MEDLINE (Ovid), Embase (Ovid), and Compendex were queried for results from 2012 to May 19, 2022. Of 2642 studies screened, 185 were included. The majority (n = 109, 60%) were US-based and involved either simulated surgical procedures using inanimate models (n = 88), virtual reality (n = 72), or intraoperative performance assessments of robotic surgeries in humans (n = 44); 36 studies combined two or more of these settings. Performance errors were explicitly defined in 104 articles (56%), and 64 used previously defined performance rating scales. The method of rating was split between manual (n = 137) and automated ratings (n = 85). Measures of performance vary considerably. More conceptual work is warranted to explicitly define errors that can inform robotic skill assessment. This is important given the growing interest in developing efficient and reliable objective measures of performance which are likely to rely on automated assessment methods.

Indexed as

Clinical CompetenceRobotic Surgical ProceduresAnimalsHumansVirtual RealityClinical competenceIntraoperative periodRobotic surgical procedureSurgical errorTask performance

Identifiers

What OpenQuestion holds

Textmetadata
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.