INTRODUCTION
Virtual reality in surgery
When first introduced, laparoscopic surgery was associated with many complications. The surgeons, experts in their fields, found that when they performed the same procedure through the minimally-invasive route, their performance did not transfer to this new technique1. However, the ethical implications of learning using humans and the legal risks during such a process must be considered2, as training during live surgery exposes the patient to the inherent risks of an inexperienced surgeon3. This led to the creation of skills labs, which allowed surgeons to develop basic skills without putting patients at risk1.
Surgical education plays a very important role in patient care4. Virtual reality (VR) simulation was first introduced in surgical education in the late 1980s. After that, many virtual reality simulators emerged, allowing students to experience a more real contact with the practice of medical procedures. As a result, VR surgical education has been largely integrated into minimally-invasive surgery (MIS) training. The MIS revolution has forced the surgical community to rethink how they train residents and adapt to new technologies5.
As demonstrated in laparoscopy, moving the venue for the acquisition of a reliable basic skill set out in the operating room and into the simulation laboratory has significant advantages for trainees, hospitals and patients alike6. It provides the surgeon with the adequate tools to train in a risk-free environment and bridges the gap between the safe acquisition of surgical skills and effective performance during live robot-assisted surgery3.
Robotic training poses several unique challenges to educators, trainees and training program directors7. The da Vinci Skills Simulator® (dVSS) (Intuitive Surgical Inc., Sunnyvale, CA), also referred to as the ´Backpack´, is a customized computer package that attaches to the actual surgical console through a single fiber optic network cable8. There are no general recommendation guidelines for the best training modality for surgeons and residents learning to use the da Vinci robot®(9. However, it is possible to verify that several countries and medical institutions have organized themselves to create their own training guidelines.
Current simulators enable trainees to practice psychomotor skills and basic procedural skills10. Taking into consideration the effectiveness of the virtual reality surgical simulators, new uses for this tool have been suggested. Since robotic surgical skills are unique and not derivative from either open or laparoscopic surgery6, would it be possible to acquire laparoscopic skills using a robotics simulator?
Learning styles (LS)
Learning is an acquired appropriate response to a stimulus, which tends to change the organism’s environment. It has been long known that several factors can influence learning in medical education, including teacher-, system-, and student-related factors. Understanding the student-related factors is crucial to facilitate learning, as well as to improve teacher- and system-related factors. The learning style is an individual’s natural or habitual pattern of acquiring and processing information in learning situations. it varies from student to student and from time to time. The students’ approaches to learning can be influenced by the perception they gain from their learning environment11. To overcome the disadvantages of treating all students in the same way, knowledge of their learning styles seems helpful to educators12. Teaching strategies associated with interpersonal intelligence should be stimulated, which could increase academic performance and encourage engagement in the learning process13.
Although a large number of LS and strategies are formulated based upon various psychological constructs, educators are interested in identifying learners based on the visual (V), auditory (A), read/write (R), or kinesthetic (K) preferences of learning14. Fleming and Miles defined those four sensory modalities of learning, which are referred together as VARK12. Because of the diversity in LS, students often find a mismatch between their learning and the delivery of instruction12. One characterization of LS is to define the learners’ preferred mode of learning in terms of the sensory modality through which they prefer to take in new information15. This knowledge can also be a useful asset in identifying the learning problems of students and making them effective learners. Students with a visual preference prefer to explain concepts by drawing pictures and diagrams12.
Conceptually defined, style is a general term encompassing all studies related to recognizing individual learning differences16. These stylistic differences can be investigated by using the Gregorc Style Delineator (GSD), based on a theory known as Mind Styles. The GSD focuses on the cognitive abilities of perception and ordering. Arranged via a quaternary design, the GSD sums the rank order of 10 sets of 4 words, thereby creating the Concrete Sequential (CS), Abstract Sequential (AS), Abstract Random (AR), and Concrete Random (CR) mind styles16.
Synchronizing a teaching style with the students’ learning preferences may bring additional benefits for them12. There is a lack of studies proving that different learning styles make it easy or difficult to develop their skills in a virtual reality environment in surgery. Robotic surgery is a minimally-invasive surgical platform, and its impact on medical student education has not yet been elucidated17.
METHODS
A prospective, longitudinal, randomized and controlled study, approved by the Research Ethics Committee was conducted. The VARK (2017) questionnaire and the multiple intelligences questionnaire proposed by Gregorc (Mind Styles) were acquired online. After printing, each participating student received one copy of each quiz.
Intern medical students from the Erasto Gaertner Hospital, aged between 18 and 25 years old, attending between the second and fourth years of medical school in the city of Curitiba, state of Paraná, Brazil, were included. All those with experience in virtual reality or video games, those with degenerative diseases or visual disorders were excluded. Students that did not complete all the planned steps were also excluded.
The students were invited to participate in the project by e-mail. Sixty-three students were selected according to the inclusion criteria and then randomized into control (C), laparoscopy (L) and robotics (R) groups. All of them signed an informed consent form. The sample number was directly related to the number of interns available to participate, as well as the availability of the simulator for the study.
Sixty-one students were included. Randomization was performed right after the students’ check-in. Subsequently, they were informed of the study schedule and objectives. A pre-test was then applied, which included ten true-or-false statements regarding the operative technique. At the end, a thirty-minute lecture was given, with surgical-clinical content. A post-test, containing the same questions as the pre-test was then applied, as well as version 7.8 of the VARK questionnaire (2017) and the Mind styles. The pre-test, post-test and questionnaires were common to all groups. At the end of the post-test, the students in the L group were referred to a surgical knot exercise in the Johnson & Johnson® laparoscopic box for sixty minutes. The students in the R group were referred to perform the same exercise for the same period, but on the dVSS® platform. The C group did not train. After the end of the activities, an exercise in the laparoscopy box, called ‘practical test’, was applied to all students, in which the students’ performances were evaluated. The students from the L and R groups, at the end of the sixty-minute training, and those in C group, who did not train, had the objective of performing a surgical knot in the laparoscopy box within a ten-minute period. For this purpose, a specific evaluation form was created, considering time to perform the exercise and occurrence of critical errors (falls, breaking the wire, lacerating the prototype, number of attempts). A scoring system was created to generate a final score for each student.
In the end, two students from the L group did not complete all the steps and were excluded from the study, totaling nineteen students in this group. The C and R groups remained with twenty-one students each.
Statistical Analysis
Considering the asymmetric distribution of most quantitative variables, these were represented by the median and interquartile range, and the qualitative variables by absolute and relative frequencies. The Kruskal-Wallis test was used when comparing values of quantitative variables between the 3 groups, , followed by the Dunn-Bonferroni test for multiple comparisons when necessary. Fisher’s exact test was used when comparing qualitative variables between the 3 groups. Wilcoxon’s signed rank test for paired data was used when comparing values of quantitative variables within the same group, whereas the Mann-Whitney U test was used when comparing values between two independent groups.
RESULTS
Description and Comparison between Groups
All variables concerning the selected sample and each group are described in Table 1. There was a significant difference between the pretest scores in the L and R groups (Dunn-Bonferroni test p-value = 0.038).
Fisher’s exact test found a significant difference between the proportions of students who managed to pass the thread in each group, with the C group showing the lowest proportion.
Variable | Entire sample | C Group | L Group | R Group | p-value | |
---|---|---|---|---|---|---|
Mind Styles | CS | 34 | 15 (71.4%) | 9 (47.4%) | 10 (47.6%) | 0.229 |
CR | 6 | 2 (9.5%) | 2 (10.5%) | 2 (9.5%) | 1 | |
AS | 16 | 3 (14.3%) | 5 (26.3%) | 8 (38.1%) | 0.22 | |
AR | 17 | 7 (33.3%) | 4 (21.1%) | 6 (28.6%) | 0.722 | |
VARK | V | 13 | 5 (23.8%) | 3 (15.8%) | 5 (23.8%) | 0.793 |
A | 23 | 6 (28.6%) | 8 (42.1%) | 9 (42.9%) | 0.637 | |
R | 14 | 6 (28.6%) | 5 (26.3%) | 3 (14.3%) | 0.553 | |
K | 22 | 11 (52.4) | 5 (26.3%) | 6 (28.6%) | 0.197 | |
Pre-test | 80 (75; 85) | 80 (75; 85) | 85 (82.5; 90) | 75 (70; 85) | 0.045 | |
Post-test | 85 (80; 95) | 85 (80; 90) | 90 (85; 85) | 85 (85; 95) | 0.507 | |
Post-test - Pre-test | 5 (0; 10) | 5 (-5; 10) | 5 (0; 10) | 10 (5; 15) | 0.07 | |
Emergency room hours training | 0 (0; 200) | 80 (0; 430) | 0 (0; 6) | 0 (0; 72) | 0.07 | |
College semesterr | 6 (5; 7) | 6 (5; 7) | 6 (4; 6) | 6 (5; 7) | 0.292 | |
Operating technique discipline | 38 | 13 (61.9%) | 11 (57.9%) | 14 (66.7%) | 0.944 | |
Needle dropping | 55 | 20 (95.2%) | 17 (89.5%) | 18 (85.7%) | 0.673 | |
Prototype laceration | 19 | 5 (23.8%) | 6 (31.6%) | 8 (38.1%) | 0.615 | |
Completed knot | 6 | 2 (9.5%) | 4 (21.1%) | 0 | 0.07 | |
Number of attempts | 2 (1; 3) | 2 (1; 4) | 3 (1.5; 4) | 2 (1; 2) | 0.44 | |
Thread passage | 45 | 11 (52.4%) | 17 (89.5%) | 17 (81%) | 0.025 | |
Practical test score | 9 (9; 12) | 11 (9; 12) | 9 (8; 11.5) | 9 (9; 12) | 0.314 |
Source: the authors (2022).
Comparison of scores within each group
The differences between the pre-test and post-test scores for the L and R groups were statistically significant according to the Wilcoxon signed rank test. Both the L and R groups had significantly higher scores in the post-test, compared with the pretest. The median increase was 5 points for the L group, and 10 points for the R group (Table 2).
Comparison of Scores According to Mind Styles and VARK
For each group, the pre-test and post-test scores and the difference between the two for each Mind Styles (Table 3) and each VARK category (Table 4) were compared. As the same participant could be classified into more than one Mind Styles category or more than one VARK category, comparisons were performed for each category separately. Whenever possible, the p-value associated with the Mann-Whitney U test was calculated, but no statistically significant differences were found.
C Group | ||||||
---|---|---|---|---|---|---|
Mind Styles | Pre-test | p-value | Post-test | p-value | Post-test - Post-test | p-value |
CS | 85 (77.5; 85) | 0.383 | 90 (80; 92.5) | 0.383 | 5 (0; 7.5) | 0.525 |
Others | 77.5 (75; 80) | 82.5 (76.2; 88.7) | -2.5 (-5; 7.5) | |||
CR | 87.5 (83.7; 91.2) | 0.299 | 85 (82; 87.5) | 0.903 | -2.5 (-3.7;-1.2) | 0.245 |
Others | 80 (75; 85) | 85 (80; 92.5) | 5 (-2.5; 10) | |||
AS | 80 (77.5; 85) | 0.918 | 75 (72.5; 85) | 0.384 | -5 (-5; 0) | 0.218 |
Others | 80 (75; 85) | 87.5 (80; 90) | 5 (0; 10) | |||
AR | 80 (72.5; 85) | 0.543 | 80 (75; 90) | 0.305 | 0 (-5; 7.5) | 0.469 |
Others | 82.5 (76.2; 85) | 90 (81.2; 90) | 5 (0; 8.7) | |||
L Grupo | ||||||
Mind Styles | Pre-test | p-value | Post-test | p-value | Post-test - pre-test | p-value |
CS | 85(85; 85) | 0.67 | 90 (85; 95) | 0.737 | 5 (0; 10) | 0.833 |
Others | 85 (76.2; 90) | 87.5 (85; 93.7) | 5 (0; 10) | |||
CR | 92.5 (91.2; 93.7) | 0.128 | 95 (95; 95) | 0.152 | 2.5 (1.2; 3.7) | 0.631 |
Others | 85 (80; 85) | 85 (85; 90) | 5 (0; 10) | |||
AS | 80 (70; 85) | 0.135 | 85 (85; 90) | 0.296 | 10 (0; 10) | 0.391 |
Others | 85 (85; 93.7) | 90 (85; 95) | 5 (0; 8.7) | |||
AR | 87.5 (82.5; 91.2)) | 0.639 | 90 (85; 96.2) | 0.505 | 5 (3.7; 6.2) | 0.918 |
Others | 85 (82.5; 87.5) | 90 (85; 92.5) | 5 (0; 10) | |||
R Grupo | ||||||
Mind Styles | Pre-test | p-value | Post-test | p-value | Post-test - pre-test | p-value |
CS | 80 (71.2; 88.7) | 0.315 | 92.5 (81.5; 95) | 0.331 | 10 (1.2; 13.7) | 0.542 |
Others | 75 (70; 77.5) | 85 (85; 90) | 10 (7.5; 17.5) | |||
CR | 70 (67.5; 72.5) | 0.222 | 85 (85; 85) | 0.581 | 15 (12.5; 17.5) | 0.428 |
Others | 75 (70; 85) | 90 (82.5; 95) | 10 (5; 15) | |||
AS | 75 (70; 81.2) | 0.684 | 90 (85; 91.2) | 0.317 | 12.5 (10; 16.2) | 0.238 |
Others | 75 (70; 90) | 85 (80; 95) | 10 (0; 15) | |||
AR | 75 (75; 82.5) | 0.721 | 85 (81.2; 88.7) | 0.381 | 10 (6.2; 13.7) | 0.552 |
Others | 75 (70; 82.5) | 90 (85; 95) | 10 (5; 20) |
Source: the authors (2022).
C Group | ||||||
---|---|---|---|---|---|---|
VARK | Pre-test | p-value | Post-test | p-value | Post-test - Pre-test | p-value |
V | 80 (80; 85) | 0.801 | 90 (85; 90) | 0.674 | 0 (-5; 10) | 0.933 |
Others | 80 (75; 85) | 85 (80; 91.2) | 5 (-1.2; 6.2) | |||
A | 80 (75; 85) | 0.812 | 82.5 (72.5; 92.5) | 0.525 | 2.5 (0; 5) | 0.781 |
Others | 80 (77.5; 85) | 90 (80; 90) | 5 (-5; 10) | |||
R | 80 (76.2; 83.7 | 0.968 | 87.5 (81.2; 93.7) | 0.5 | 5 (5; 5) | 0.404 |
Others | 80 (75; 85) | 85 (77.5; 90) | 0 (-5; 10) | |||
K | 85 (75; 87.5) | 0.566 | 85 (80; 92.5) | 0.943 | 5 (-5; 7.5) | 0.829 |
Others | 80 (76.2; 83.7) | 87.5 (81.2; 90) | 2.5 (0; 8.7) | |||
L Grupo | ||||||
VARK | Pre-test | p-value | Post-test | p-value | Post-test - Pre-test | p-value |
V | 85 (85; 90) | 0.521 | 95 (90; 97.5) | 0.251 | 0 (0; 7.5) | 1 |
Others | 85 (78.7; 90) | 87.5 (85; 91.2) | 5 (0; 10) | |||
A | 85 (70; 87.5) | 0.344 | 90 (83.7; 95) | 0.865 | 7.5 (5; 10) | 0.089 |
Others | 85 (85; 90) | 85 (85; 92.5) | 0 (0; 7.5) | |||
R | 85 (80; 85) | 0.247 | 85 (85; 90) | 0.296 | 5 (0; 10) | 0.886 |
Others | 85 (85; 93.7) | 90 (85; 95) | 5 (0; 10) | |||
K | 90 (85; 95) | 0.067 | 90 (85; 95) | 0.635 | 0 (0; 5) | 0.115 |
Others | 85 (76.2; 85) | 87.5 (85; 93.7) | 7.5 (0; 10) | |||
R Grupo | ||||||
VARK | Pre-test | p-value | Post-test | p-value | Post-test - Post-test | p-value |
V | 85 (70; 90) | 0.584 | 80 (75; 90) | 0.237 | 10 (0; 10) | 0.13 |
Others | 75 (70; 80) | 87.5 (85; 95) | 12.5 (5; 20) | |||
A | 75 (75; 80) | 0.744 | 85 (85; 90) | 0.327 | 10 (5; 15) | 1 |
Others | 77.5 (70; 86.2) | 90 (83.7; 95) | 10 (5; 16.2) | |||
R | 80 (75; 82.5) | 0.837 | 85 (77.5; 90) | 0.644 | 10 (0; 12.5) | 0.609 |
Others | 75 (70; 83.7) | 87.5 (85; 93.7) | 10 (5; 18.7) | |||
K | 75 (71.2; 78.7) | 0.874 | 92.5 (86.2; 95) | 0.095 | 15 (6.2; 20) | 0.267 |
Others | 75 (70; 85) | 85 (80; 90) | 10 (2.5; 15) |
Source: the authors (2022).
Scores in the practical test
Considering the three groups together, the practical test scores between the different Mind Styles and the different VARK learning styles were compared in the same way as the other test scores. No significant differences were found, indicating that the practical test scores were relatively homogeneous between groups and between the Mind Styles and VARK categories (Table 5).
Mind Styles | Score in the - practical test | p-value | VARK | Score in the - practical test | p-value |
---|---|---|---|---|---|
CS | 10 (9; 12) | 0.841 | V | 9 (9; 10) | 0.449 |
Others | 9 (8; 12) | Others | 11 (8; 12) | ||
CR | 10 (9; 11.7) | 1 | A | 9 (8; 12) | 0.921 |
Others | 9 (9; 12) | Others | 9.5 (9; 12) | ||
AS | 12 (9; 12) | 0.189 | R | 11.5 (9.2; 12) | 0.334 |
Others | 9 (8; 12) | Others | 9 (9; 12) | ||
AR | 9 (8; 12) | 0.315 | K | 9 (8.2; 12) | 0.896 |
Others | 10.5 (9; 12) | Others | 10 (9; 12) |
Source: the authors (2022).
The same procedure was performed within each group, comparing each Mind Styles with the others, and each VARK category with the others.
No significant difference was found, indicating that the practical test scores were relatively homogeneous between the groups and between the Mind Styles and VARK categories.
DISCUSSION
When the idea of this research with medical students came up, the first question that was raised was the adherence. Higgins et al. found that the robotic operating room experience is demotivating for medical students. There is little opportunity for mastery, autonomy, and relationship development17. However, in our study, the vast majority showed interest and motivation to participate. Another challenge was related to the availability of the dVSS® for training, as our robotic training center was the first in the state of Paraná and, therefore, an essential part of the implementation of other robotic services and training of surgeons in the technology.
All aspects of medical education have been severely impacted by the pandemic. There is an increased interest in simulation programs at home, which could ensure the continuity of technical skills training during the COVID-19 pandemic, particularly in highly technical and demanding surgical specialties18.
During the COVID-19 pandemic, online medical training including simulated clinical scenarios prevented training interruption and the majority of the participating students had a positive attitude regarding the perceived quality of this training modality19. In addition, the COVID pandemic limited the viability of face-to-face meetings in the previously approved model, which enabled the first adaptation - increase of preventive measures and social distancing.
The dVSS® can be integrated with the existing da Vinci Xi or Si surgeon consoles, thus providing a practice platform to be used inside or outside the operating room, without requiring additional system components20. Although there are other available platforms, this was the only one used in this study.
Each platform has the capacity to train and assess a variety of different robotic skills fundamental to the technique7. It can be used to assess basic robotic skills, as well as the first step in training, before moving on to more advanced tasks21.
A final opportunity to optimize medical student learning in robotic surgery is simulation. An additional component of robotic surgery that impacts the medical student learning environment is the influence of other students, who are also navigating through the technology’s learning curve17.
Unfortunately, most of the currently available exercises in VR simulators are generic tasks testing hand - eye coordination, tissue manipulation, dissection, suturing and knot tying. There is no evidence to suggest which exercises lead to improved real-setting performance7.
From the evidence available, it seems that simulation-based training does result in skills transfer to the operative setting. Simulation-based training therefore provides a safe, effective, and ethical way for trainees to acquire surgical skills before entering the operating room22.
Despite a lack of evidence for a direct relationship between VR simulation and performance on actual human cases, it has been well described that the skills gained from VR training are similar to those attained via traditional robotic dry laboratory simulation training6.
Laparoscopic surgery requires working in a three-dimensional environment with a two-dimensional view. Skills such as depth perception, hand-to-eye coordination and bimanual manipulation are crucial to its efficacy1.
This makes the development of skills in standard simulators very challenging, raising the question of how much virtual reality simulators can help in the learning curve. Nonetheless, training in virtual reality simulators versus ‘standard’ laparoscopic training (the traditional apprenticeship model) did not reveal any difference in the overall operating time and complication rates (measured by number of cases converted to open surgery). Performance was assessed by parameters such as tissue handling, path length of instruments and keeping the instruments within the field of vision1.
Training in laparoscopy, notably the Fundamentals of Laparoscopy Surgery (FLS) curriculum, is more accessible to surgery residents, but evidence is lacking as to whether these skills transfer to robot-assisted surgery23.
Few studies have compared medical students’ exposure to virtual reality training in robotic and laparoscopy surgeons. And according to Vurgun, medical students’ initial experience with robot-assisted surgery did not differ significantly after limited laparoscopy exposure23.
Our study did not demonstrate any statistical significance when comparing skill gains between the studied groups.
Most of the studies assess surgeons’ skills. Pimentel et al. supported the concept that the fundamental techniques of robot-assisted surgery are not influenced by the surgeon's experience in laparoscopic surgery. This may be explained by the fact that the skills required for robotic surgery are different from those acquired in laparoscopic surgery training. There are no significant differences in the performance of simulated robotic surgical tasks between surgeons with laparoscopic training and surgical residents24.
Altogether, Vurgun et al., suggested that exposure to laparoscopic training, in the form of limited psychomotor skills training, does not affect initial robot-assisted surgical performance among students and supports the idea that training in robotic surgery ought to take place in a robot-assisted simulation environment23.
In medical studies, both theoretical and practical expertise have a vital role, while repetition of hands-on practice can improve the young doctors’ professional competency. Virtual reality was found to be the best for medical students regarding both learning motivation and learning competency. Medical students and teachers may select virtual reality as a new learning methodology for curriculum learning25.
Learning styles based on four sensory modalities of VARK were described by Fleming. A visual student prefers the visual mode, i.e., through seeing, whereas an aural student prefers listening techniques. Read/write students prefer reading and writing for assimilating and accommodating the information. A kinesthetic student experiences learning by performing tasks . According to Valerdi et al., multimodal-type students may be in a situation that they can shift from mode to mode, depending on the context or are satisfied only when they have had their input in all their preferred modes11.
Students with an aural preference prefer to receive or give information by listening and talking. Students with a read-write preference can easily understand concepts using lists, booklets, and textbooks. Students with a kinesthetic preference favor a hands-on approach, trial and error, and real-life examples12.
Parashar et al. compared learning styles among students using Friedman’s test. The pattern of learning styles was different, and some learning styles were more often preferred than others; this difference was statistically significant (P<0.001). In this study, aural and kinesthetic styles were preferred over other styles by the students11. The same results were found in our study.
The learning style varies from one group to another based on culture, the nature of the studies and the students’ characteristics. A study carried out in Malaysia showed that the mean VARK scores of kinesthetic and read/write students were higher than those of auditory and visual students14.
Knowledge of the students’ learning styles and the characteristics that affect them is important for teachers to improve lesson plans and develop teaching methodologies to adapt them to their students’ needs. Khanal et al. showed that the majority of the medical students (53.52%) were multimodal students, with more than one VARK component. Among unimodal students, most of them were kinesthetic learners (29.6%), followed by aural, visual and read/write students12.
Learning styles may change over a shorter time frame than over the course of a medical degree. Learning styles may indeed change based on the context, environment and topic being learned and it is likely a flexible changing trait, rather than a fixed innate trait exhibited by a student26.
Another way to assess learning styles is using the GSD. The GSD focuses on the cognitive abilities of perception and ordering. Arranged via a quaternary design, the GSD sums the rank order of 10 sets of 4 words, thereby creating the Concrete Sequential (CS), Abstract Sequential (AS), Abstract Random (AR), and Concrete Random (CR) mind styles. The CS individual prefers physical, hands-on tasks that are structured (e.g., repair technician). The AS individual prefers reflective thinking tasks that provide an expression of intellect and rationality (e.g., academician). The AR individual prefers nonphysical tasks that allow emotional and interpretive expression (e.g., poetic writer). The CR individual prefers investigative tasks that incorporate risk taking or multiple options (e.g., cinematographer)16.
The undergraduate students assessed in this study showed the following distribution of styles: 89 (44.5%) were CS, 21 (10.5%) were AS, 54 (27%) were AR, and 36 (18%) were CR. These results support the findings of Gregorc, who reported the CS mind style to be the most commonly preferred, followed by the AS, AR, and CR styles, respectively16. In our study, we found similar distribution of styles.
Several studies have previously reported faster learning curves and improved retention of skills with robotic assistance as compared to laparoscopy regarding basic manipulation tasks, and improved task speeds with robotic assistance have been measured as compared to laparoscopy, but with minimal transfer effects. Other studies have stated that skills transfer effects from laparoscopy to robotic surgery may be more pronounced with difficult tasks, such as suturing. In our viewpoint, laparoscopy and robotic surgery are different domains, perhaps requiring different skills23.
STUDY LIMITATIONS
The first limitation of this study was the number of included students. A larger number of participants would improve the results. One reason is that the medical students were from a single institution. Furthermore, with the COVID pandemic and the low availability of the robotics simulator, participation was limited, as was the possibility of extending the training time. The use of a standard assessment method could facilitate data analysis or standardize them using assessments previously described in the literature.