Evidence map›Paper›PMID 37603102›Full record

ArticleSurgical endoscopy2023

Baseline performance in a robotic virtual reality platform predicts rate of skill acquisition in a proficiency-based curriculum: a cohort study of surgical trainees.

Andres A Abreu, Benjamin Rail, Emile Farah, Rodrigo E Alterio, Daniel J Scott, Ganesh Sankaranarayanan, Herbert J Zeh, Patricio M Polanco

Abstract read
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Article in Surgical endoscopy, 2023. 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.

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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

8 authors.

Andres A AbreuDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Benjamin RailDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Emile FarahDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Rodrigo E AlterioDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Daniel J ScottDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Ganesh SankaranarayananDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Herbert J ZehDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
Patricio M PolancoDivision of Surgical Oncology, Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA. Patricio.polanco@utsouthwestern.edu.ORCID 0000-0002-1719-1208

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResidency programs must prepare to train the next generation of surgeons on the robotic platform. The purpose of this study was to determine if baseline skills of residents on a virtual reality (VR) robotic simulator before intern year predicted future performance in a proficiency-based curriculum.

methodsAcross two academic years, 21 general surgery PGY-1s underwent the robotic surgery boot camp at the University of Texas Southwestern. During boot camp, subjects completed five previously validated VR tasks, and their performance metrics (score, time, and economy of motion [EOM]) were extracted retrospectively from their Intuitive learning accounts. The same metrics were assessed during their residency until they reached previously validated proficiency benchmarks. Outcomes were defined as the score at proficiency, attempts to reach proficiency, and time to proficiency. Spearman's rho and Mann-Whitney U tests were used; median (IQR) was reported. Significance level was set at p < 0.05.

resultsTwenty-one residents completed at least three out of the five boot camp tasks and achieved proficiency in the former during residency. The median average score at boot camp was 12.3 (IQR: 5.14-18.5). The median average EOM at boot camp was 599.58 cm (IQR: 529.64-676.60). The average score at boot camp significantly correlated with lower time to achieve proficiency (p < 0.05). EOM at boot camp showed a significant correlation with attempts to proficiency and time to proficiency (p < 0.01). Residents with an average baseline EOM below the median showed a significant difference in attempts to proficiency (p < 0.05) and time to proficiency (p < 0.05) compared to those with EOMs above or equal to the median.

conclusionResidents with an innate ability to perform tasks with better EOM may acquire robotic surgery skills faster. Future investigators could explore how these innate differences impact performance throughout residency.

Indexed as

Internship and ResidencyRobotic Surgical ProceduresVirtual RealityClinical CompetenceCohort StudiesCurriculumHumansRetrospective StudiesCurriculum developmentRobotic surgerySimNowSimulationSurgical skills

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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.