Literature DB >> 29052071

Validation of the VBLaST pattern cutting task: a learning curve study.

Ali M Linsk1, Kimberley R Monden2, Ganesh Sankaranarayanan2, Woojin Ahn3, Daniel B Jones1, Suvranu De3, Steven D Schwaitzberg4, Caroline G L Cao5.   

Abstract

BACKGROUND: Mastery of laparoscopic skills is essential in surgical practice and requires considerable time and effort to achieve. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PC©) is a virtual simulator that was developed as a computerized version of the pattern cutting (PC) task in the Fundamentals of Laparoscopic Surgery (FLS) system. To establish convergent validity for the VBLaST-PC©, we assessed trainees' learning curves using the cumulative summation (CUSUM) method and compared them with those on the FLS.
METHODS: Twenty-four medical students were randomly assigned to an FLS training group, a VBLaST training group, or a control group. Fifteen training sessions, 30 min in duration per session per day, were conducted over 3 weeks. All subjects completed pretest, posttest, and retention test (2 weeks after posttest) on both the FLS and VBLaST© simulators. Performance data, including time, error, FLS score, learning rate, learning plateau, and CUSUM score, were analyzed.
RESULTS: The learning curve for all trained subjects demonstrated increasing performance and a performance plateau. CUSUM analyses showed that five of the seven subjects reached the intermediate proficiency level but none reached the expert proficiency level after 150 practice trials. Performance was significantly improved after simulation training, but only in the assigned simulator. No significant decay of skills after 2 weeks of disuse was observed. Control subjects did not show any learning on the FLS simulator, but improved continually in the VBLaST simulator.
CONCLUSIONS: Although VBLaST©- and FLS-trained subjects demonstrated similar learning rates and plateaus, the majority of subjects required more than 150 trials to achieve proficiency. Trained subjects demonstrated improved performance in only the assigned simulator, indicating specificity of training. The virtual simulator may provide better opportunities for learning, especially with limited training exposure.

Entities:  

Keywords:  Convergent validity; Cumulative summation (CUSUM); Learning curve; Surgical training; Virtual reality

Mesh:

Year:  2017        PMID: 29052071      PMCID: PMC5845471          DOI: 10.1007/s00464-017-5895-0

Source DB:  PubMed          Journal:  Surg Endosc        ISSN: 0930-2794            Impact factor:   4.584


  47 in total

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3.  Preliminary evaluation of the pattern cutting and the ligating loop virtual laparoscopic trainers.

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Journal:  Surg Endosc       Date:  2014-08-27       Impact factor: 4.584

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6.  Learning curves in laparoscopic right-sided colon cancer surgery: a comparison of first-generation colorectal surgeon to advance laparoscopically trained surgeon.

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7.  Development and validation of VBLaST-PT© : a virtual peg transfer simulator.

Authors:  Venkata S Arikatla; Ganesh Sankaranarayanan; Woojin Ahn; Amine Chellali; Caroline G L Cao; Suvranu De
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Authors:  Venkata S Arikatla; Ganesh Sankaranarayanan; Woojin Ahn; Amine Chellali; Suvranu De; G L Caroline; John Hwabejire; Marc DeMoya; Steven Schwaitzberg; Daniel B Jones
Journal:  Surg Endosc       Date:  2012-12-14       Impact factor: 4.584

9.  Preliminary face and construct validation study of a virtual basic laparoscopic skill trainer.

Authors:  Ganesh Sankaranarayanan; Henry Lin; Venkata S Arikatla; Maureen Mulcare; Likun Zhang; Alexandre Derevianko; Robert Lim; David Fobert; Caroline Cao; Steven D Schwaitzberg; Daniel B Jones; Suvranu De
Journal:  J Laparoendosc Adv Surg Tech A       Date:  2010-03       Impact factor: 1.878

10.  Laparoscopic appendectomy: a junior trainee's learning curve.

Authors:  Usman Jaffer; Alan E P Cameron
Journal:  JSLS       Date:  2008 Jul-Sep       Impact factor: 2.172

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  5 in total

1.  Characterizing the learning curve of a virtual intracorporeal suturing simulator VBLaST-SS©.

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Journal:  Surg Endosc       Date:  2019-09-03       Impact factor: 4.584

2.  Learning rate of students detecting and annotating pediatric wrist fractures in supervised artificial intelligence dataset preparations.

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3.  A machine learning approach to predict surgical learning curves.

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Journal:  Surgery       Date:  2019-11-18       Impact factor: 3.982

4.  Training on a virtual reality cricothyroidotomy simulator improves skills and transfers to a simulated procedure.

Authors:  Ganesh Sankaranarayanan; Coleman A Odlozil; Salman S Hasan; Rehma Shabbir; Di Qi; Melih Turkseven; Suvranu De; Geoffrey Funk; Rebecca J Weddle
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5.  Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology.

Authors:  Anil Kamat; Basiel Makled; Jack Norfleet; Steven D Schwaitzberg; Xavier Intes; Suvranu De; Anirban Dutta
Journal:  NPJ Sci Learn       Date:  2022-08-25
  5 in total

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