Construct, content and face validity of the eoSim laparoscopic simulator on advanced suturing tasks

Leijte, Erik; Arts, Elke; Witteman, Bart; Jakimowicz, Jack J.; Blaauw, Ivo de; Botden, Sanne M. B. I.

doi:10.1007/s00464-018-06652-3

Cited by 30 publications

(24 citation statements)

References 20 publications

(15 reference statements)

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“…• simulating the surgical reality; As employed previously in the literature [16][17][18][19][20][21], for the validation of the LABOT, we used the following validity models: "face validity," "content validity," and "construct validity" [22,23].…”

Section: Validation Processmentioning

confidence: 99%

Development and Validation of a Homemade, Low-Cost Laparoscopic Simulator for Resident Surgeons (LABOT)

Soriero

Atzori

Barra

et al. 2020

IJERPH

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Several studies have demonstrated that training with a laparoscopic simulator improves laparoscopic technical skills. We describe how to build a homemade, low-cost laparoscopic training simulator (LABOT) and its validation as a training instrument. First, sixty surgeons filled out a survey characterized by 12 closed-answer questions about realism, ergonomics, and usefulness for surgical training (global scores ranged from 1—very insufficient to 5—very good). The results of the questionnaires showed a mean (±SD) rating score of 4.18 ± 0.65 for all users. Then, 15 students (group S) and 15 residents (group R) completed 3 different tasks (T1, T2, T3), which were repeated twice to evaluate the execution time and the number of users’ procedural errors. For T1, the R group had a lower mean execution time and a lower rate of procedural errors than the S group; for T2, the R and S groups had a similar mean execution time, but the R group had a lower rate of errors; and for T3, the R and S groups had a similar mean execution time and rate of errors. On a second attempt, all the participants tended to improve their results in doing these surgical tasks; nevertheless, after subgroup analysis of the T1 results, the S group had a better improvement of both parameters. Our laparoscopic simulator is simple to build, low-cost, easy to use, and seems to be a suitable resource for improving laparoscopic skills. In the future, further studies should evaluate the potential of this laparoscopic box on long-term surgical training with more complex tasks and simulation attempts.

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Section: Validation Processmentioning

confidence: 99%

Development and Validation of a Homemade, Low-Cost Laparoscopic Simulator for Resident Surgeons (LABOT)

Soriero

Atzori

Barra

et al. 2020

IJERPH

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“…The questionnaire in this study has been used in the previous validation studies, but has been adjusted to evaluate tasks performed on the RobotiX simulator [21][22][23][24]. The questionnaire was divided in two sections (see Supplemental 1).…”

Section: Questionnairementioning

confidence: 99%

Training benchmarks based on validated composite scores for the RobotiX robot-assisted surgery simulator on basic tasks

et al. 2020

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The RobotiX robot-assisted virtual reality simulator aims to aid in the training of novice surgeons outside of the operating room. This study aimed to determine the validity evidence on multiple levels of the RobotiX simulator for basic skills. Participants were divided in either the novice, laparoscopic or robotic experienced group based on their minimally invasive surgical experience. Two basic tasks were performed: wristed manipulation (Task 1) and vessel energy dissection (Task 2). The performance scores and a questionnaire regarding the realism, didactic value, and usability were gathered (content). Composite scores (0-100), pass/fail values, and alternative benchmark scores were calculated. Twenty-seven novices, 21 laparoscopic, and 13 robotic experienced participants were recruited. Content validity evidence was scored positively overall. Statistically significant differences between novices and robotic experienced participants (construct) was found for movements left (Task 1 p = 0.009), movements right (Task 1 p = 0.009, Task 2 p = 0.021), path length left (Task 1 p = 0.020), and time (Task 1 p = 0.040, Task 2 p < 0.001). Composite scores were statistically significantly different between robotic experienced and novice participants for Task 1 (85.5 versus 77.1, p = 0.044) and Task 2 (80.6 versus 64.9, p = 0.001). The pass/fail score with false-positive/false-negative percentage resulted in a value of 75/100, 46/9.1% (Task 1) and 71/100, 39/7.0% (Task 2). Calculated benchmark scores resulted in a minority of novices passing multiple parameters. Validity evidence on multiple levels was assessed for two basic robot-assisted surgical simulation tasks. The calculated benchmark scores can be used for future surgical simulation training.

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“…This simulator was selected due to the objective parametric recording without the virtual reality aspect and therefore, maintaining realistic instrument force feedback. The eoSim consists of a validated inanimate box trainer setup with a laptop and supplied software, to guide the trainee and track the instrument tips (Augmented Reality) [14][15][16][17]. During training the standard supplied needle holders were used for all three tasks.…”

Section: Eosim Laparoscopic Simulatormentioning

confidence: 99%

“…The vaginal cuff closure (Task 3) was a anastomosis needle transfer task by closing the vaginal cuff with a barbed suture. The laparoscopic Task 2 and 3 on the eoSim where developed by the researchers and validated in a previous study [17]. A specific task description is found in the supplemental section of this study.…”

Section: Tasksmentioning

confidence: 99%

Robot assisted versus laparoscopic suturing learning curve in a simulated setting

Leijte¹,

Blaauw²,

Workum

et al. 2019

Surg Endosc

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Background Compared to conventional laparoscopy, robot assisted surgery is expected to have most potential in difficult areas and demanding technical skills like minimally invasive suturing. This study was performed to identify the differences in the learning curves of laparoscopic versus robot assisted suturing. Method Novice participants performed three suturing tasks on the EoSim laparoscopic augmented reality simulator or the RobotiX robot assisted virtual reality simulator. Each participant performed an intracorporeal suturing task, a tilted plane needle transfer task and an anastomosis needle transfer task. To complete the learning curve, all tasks were repeated up to twenty repetitions or until a time plateau was reached. Clinically relevant and comparable parameters regarding time, movements and safety were recorded. Intracorporeal suturing time and cumulative sum analysis was used to compare the learning curves and phases. Results Seventeen participants completed the learning curve laparoscopically and 30 robot assisted. Median first knot suturing time was 611 s (s) for laparoscopic versus 251 s for robot assisted (p < 0.001), and this was 324 s versus 165 (sixth knot, p < 0.001) and 257 s and 149 s (eleventh knot, p < 0.001) respectively on base of the found learning phases. The percentage of 'adequate surgical knots' was higher in the laparoscopic than in the robot assisted group. First knot: 71% versus 60%, sixth knot: 100% versus 83%, and eleventh knot: 100% versus 73%. When assessing the 'instrument out of view' parameter, the robot assisted group scored a median of 0% after repetition four. In the laparoscopic group, the instrument out of view increased from 3.1 to 3.9% (left) and from 3.0 to 4.1% (right) between the first and eleventh knot (p > 0.05). Conclusion The learning curve of minimally invasive suturing shows a shorter task time curve using robotic assistance compared to the laparoscopic curve. However, laparoscopic outcomes show good end results with rapid outcome improvement.

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Construct, content and face validity of the eoSim laparoscopic simulator on advanced suturing tasks

Cited by 30 publications

References 20 publications

Development and Validation of a Homemade, Low-Cost Laparoscopic Simulator for Resident Surgeons (LABOT)

Development and Validation of a Homemade, Low-Cost Laparoscopic Simulator for Resident Surgeons (LABOT)

Training benchmarks based on validated composite scores for the RobotiX robot-assisted surgery simulator on basic tasks

Robot assisted versus laparoscopic suturing learning curve in a simulated setting

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