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Surgical Endoscopy

, Volume 32, Issue 8, pp 3576–3581 | Cite as

A model for predicting the GEARS score from virtual reality surgical simulator metrics

  • Ariel Kate Dubin
  • Danielle Julian
  • Alyssa Tanaka
  • Patricia Mattingly
  • Roger Smith
Article

Abstract

Background

Surgical education relies heavily upon simulation. Assessment tools include robotic simulator assessments and Global Evaluative Assessment of Robotic Skills (GEARS) metrics, which have been validated. Training programs use GEARS for proficiency testing; however, it requires a trained human evaluator. Due to limited time, learners are reliant on surgical simulator feedback to improve their skills. GEARS and simulator scores have been shown to be correlated but in what capacity is unknown. Our goal is to develop a model for predicting GEARS score using simulator metrics.

Methods

Linear and multivariate logistic regressions were used on previously reported data by this group. Subjects performed simple (Ring and Rail 1) and complex (Suture Sponge 1) tasks on simulators, the dV-Trainer (dVT) and the da Vinci Skills Simulator (dVSS). They were scored via simulator metrics and GEARS.

Results

A linear model for each simulator and exercise showed a positive linear correlation. Equations were developed for predicting GEARS Total Score from simulator Overall Score. Next, the effects of each individual simulator metric on the GEARS Total Score for each simulator and exercise were examined. On the dVSS, Excessive Instrument Force was significant for Ring and Rail 1 and Instrument Collision was significant for Suture Sponge 1. On the dVT, Time to Complete was significant for both exercises. Once the significant variables were identified, multivariate models were generated. Comparing the predicted GEARS Total Score from the linear model (using only simulator Overall Score) to that using the multivariate model (using the significant variables for each simulator and exercise), the results were similar.

Conclusions

Our results suggest that trainees can use simulator Overall Score to predict GEARS Total Score using our linear regression equations. This can improve the training process for those preparing for high-stakes assessments.

Keywords

Surgical education Simulation Robotic simulator Assessment Predictive model 

Notes

Acknowledgements

The authors would like to thank Julie Pepe, Ph.D. of Florida Hospital for her help with statistics.

Funding

This study received financial support through a grant from the US Army Telemedicine and Advanced Technology Research Center (TATRC), Grant Number W81XWH-11-2-0158.

Compliance with ethical standards

Disclosures

Drs. Dubin, Mattingly, Tanaka, and Smith and Ms. Julian have no conflicts of interest or financial ties to disclose.

References

  1. 1.
    Riener R (2012) ‘Virtual reality in medicine’ M. Harders. Springer, LondonCrossRefGoogle Scholar
  2. 2.
    Ramos P, Montez J, Tripp A, Ng CK, Gill IS, Hung AJ (2014) Face, content, construct and concurrent validity of dry laboratory exercises for robotic training using a global assessment tool. BJU Int 113(5):836–842.  https://doi.org/10.1111/bju.12559 CrossRefPubMedGoogle Scholar
  3. 3.
    Hung AJ, Patil MB, Zehnder P, Cai J, Ng CK, Aron M et al (2012) Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study. J Urol 187(2):630–637.  https://doi.org/10.1016/j.juro.2011.09.154 CrossRefPubMedGoogle Scholar
  4. 4.
    Kiely DJ, Gotlieb WH, Lau S, Zeng X, Samouelian V, Ramanakumar AV et al (2015) Virtual reality robotic surgery simulation curriculum to teach robotic suturing: a randomized controlled trial. J Robot Surg 9(3):179–186CrossRefPubMedGoogle Scholar
  5. 5.
    Goh AC, Goldfarb DW, Sander JC, Miles BJ, Dunkin BJ (2012) Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. J Urol 187(1):247–252.  https://doi.org/10.1016/j.juro.2011.09.032 CrossRefPubMedGoogle Scholar
  6. 6.
    Hung AJ, Bottyan T, Clifford TG, Serang S, Nakhoda ZK, Shah SH et al (2017) Structured learning for robotic surgery utilizing a proficiency score: a pilot study. World J Urol 35(1):27–34.  https://doi.org/10.1007/s00345-016-1833-3 CrossRefPubMedGoogle Scholar
  7. 7.
    Stefanidis D, Sevdalis N, Paige J, Zevin B, Aggarwal R, Grantcharov T et al (2015) Simulation in surgery: what’s needed next? Ann Surg 261(5):846–853.  https://doi.org/10.1097/sla.0000000000000826 CrossRefPubMedGoogle Scholar
  8. 8.
    Dubin AK, Smith R, Julian D, Tanaka A, Mattingly P (2017) A comparison of robotic simulation performance on basic virtual reality skills: simulator subjective vs. objective assessment tools. J Minim Invasive Gynecol.  https://doi.org/10.1016/j.jmig.2017.07.019 CrossRefPubMedGoogle Scholar
  9. 9.
    Intuitive Surgical Inc (2012) Skills Simulator for the da Vinci SI Surgical SystemGoogle Scholar
  10. 10.
    Mimic Technologies Inc (2015) dV-Trainer operators manualGoogle Scholar
  11. 11.
    Perrenot C, Perez M, Tran N et al (2012) Surg Endosc 26:2587.  https://doi.org/10.1007/s00464-012-2237-0 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ariel Kate Dubin
    • 1
  • Danielle Julian
    • 2
  • Alyssa Tanaka
    • 3
  • Patricia Mattingly
    • 1
  • Roger Smith
    • 2
  1. 1.Department of Obstetrics and GynecologyColumbia University Medical CenterNew YorkUSA
  2. 2.Florida Hospital Nicholson CenterOrlandoUSA
  3. 3.SoarTech Inc.OrlandoUSA

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