Validation of the AUA BLUS Tasks

Kowalewski, Timothy M.; Sweet, Robert; Lendvay, Thomas S.; Menhadji, Ashleigh; Averch, Timothy D.; Box, Geoffrey N.; Brand, Timothy C.; Ferrandino, Michael N.; Kaouk, Jihad; Knudsen, Bodo E.; Landman, Jaime; Lee, Benjamin; Schwartz, Bradley F.; McDougall, Elspeth M.

doi:10.1016/j.juro.2015.10.087

Cited by 26 publications

(11 citation statements)

References 14 publications

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“…In other instances, learning curve connects a series of scores, given by a simulator, to the single task performed . This allows Pi measurement and weighted error assessment, but only on virtual reality platforms or through expensive data collection systems such as the EDGE .…”

Section: Introductionmentioning

confidence: 99%

“…The Pi score provides an objective measurement of Pi over a small number of trials, takes into account multiple variables and uses established quality standards as a benchmark. We performed our tests by applying the Pi score to tasks and rules from the European Association of Urology (EAU) Basic Laparoscopic Urological Skills (E‐BLUS) , a training curriculum that, just like the Fundamentals of Laparoscopic Surgery or the AUA BLUS , is standardized, low‐cost and easily replicable. E‐BLUS includes four tasks involving the most relevant basic skills in laparoscopy and can be taught in a standardized manner .…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement (Pi) score: an algorithm to score Pi objectively during E‐BLUS hands‐on training sessions. A European Association of Urology, Section of Uro‐Technology (ESUT) project

Veneziano

Canova²,

Arnolds

et al. 2018

BJU International

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ObjectiveTo evaluate the variability of subjective tutor performance improvement (Pi) assessment and to compare it with a novel measurement algorithm: the Pi score. Materials and MethodsThe Pi-score algorithm considers time measurement and number of errors from two different repetitions (first and fifth) of the same training task and compares them to the relative task goals, to produce an objective score. We collected data during eight courses on the four European Association of Urology training in Basic Laparoscopic Urological Skills (E-BLUS) tasks. The same tutor instructed on all courses. Collected data were independently analysed by 14 hands-on training experts for Pi assessment. Their subjective Pi assessments were compared for inter-rater reliability. The average per-participant subjective scores from all 14 proctors were then compared with the objective Pi-score algorithm results. Cohen's j statistic was used for comparison analysis. ResultsA total of 50 participants were enrolled. Concordance found between the 14 proctors' scores was the following: Task 1, j = 0.42 (moderate); Task 2, j = 0.27 (fair); Task 3, j = 0.32 (fair); and Task 4, j = 0.55 (moderate). Concordance between Pi-score results and proctor average scores per participant was the following: Task 1, j = 0.85 (almost perfect); Task 2, j = 0.46 (moderate); Task 3, j = 0.92 (almost perfect); Task 4 = 0.65 (substantial). ConclusionThe present study shows that evaluation of Pi is highly variable, even when formulated by a cohort of experts. Our algorithm successfully provided an objective score that was equal to the average Pi assessment of a cohort of experts, in relation to a small amount of training attempts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Improvement (Pi) score: an algorithm to score Pi objectively during E‐BLUS hands‐on training sessions. A European Association of Urology, Section of Uro‐Technology (ESUT) project

Veneziano

Canova²,

Arnolds

et al. 2018

BJU International

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“…The urology trainee was enrolled in a training program with mandatory laparoscopic simulation skills training, using both dry laboratory (laparoscopic training box) once weekly and wet laboratory simulation training (using a live pig training model) biannually. Surgicaltechnique used for training was based on the validated basic laparoscopic urologic surgery (BLUS) technique which is proposed by the AUA [9]. Learning curve was assessed in terms of operative time and the incidence of complications.…”

Section: Methodsmentioning

confidence: 99%

Learning curve of laparoscopic nephrectomy: a prospective pilot study

et al. 2020

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Background: Learning curve of laparoscopic nephrectomy (LN) is mainly affected by two main factors: plotting performance and experience. However, there is paucity in the literature addressing the number of cases required to adopt LN. Herein, we aimed to assess the learning curve of LN for various renal disorders and number of cases required to adopt the technique. Between September 2015 and December 2017, consecutive patients undergoing LN for various renal diseases were enrolled in this study. Patients were divided into two groups, the first 20 cases (group A) and subsequent 20 cases (group B). All procedures were performed by a single trainee urologist under supervision of an expert endourologist. Learning curve was assessed using operative time and incidence of complications. Results: A total of 40 patients were included in this pilot clinical study. Mean age was 38.2 ± 16.3 years. The mean operative time for patients in group B was significantly lower than the mean operative time for patients in group A (108.5 vs. 139.3 min, p < 0.05). However, there were no significant differences between both groups in terms of intraoperative blood loss (86 vs. 104 ml; p = 0.081), conversion to open surgery (5% vs. 10%; p = 0.256) and postoperative complications (5% vs. 15%; p = 0.09) for group B and group A, respectively. Similarly, there was no significant difference between both groups in terms of hospital stay (42 ± 8 vs. 46 ± 11 h p = 0.01). The trainee surgeon reached a plateau after 22 cases. Conclusions: Our study suggests that a minimum of 22 LN procedures are needed in order to adopt the technique of laparoscopic nephrectomy. Learning curve of LN is mainly affected by number of performed procedures within a short period of time.

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“…Prior work has discovered that recovering stroke patients have a decrease in 'jerky' nonsmooth movements as they progress in rehabilitation therapy [14]. The jerk cost of all tool movements performed in a task has also been used to measure accuracy and skill in areas in which skill is needed [15]. The jerk cost is computed by taking the integral of the squared jerk summed for both the left and right hand over the course of the performance, shown in Equation 1, in which T is total time of task performance and x is the magnitude of the movement.…”

Section: Objective Metricsmentioning

confidence: 99%

The effect of video playback speed on surgeon technical skill perception

et al. 2020

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Purpose: Finding effective methods of discriminating surgeon technical skill has proven a complex problem to solve computationally. Previous research has shown that obtaining non-expert crowd evaluations of surgical performances is as accurate as the gold standard, expert surgeon review [1]. The aim of this research is to learn whether crowdsourced evaluators give higher ratings of technical skill to video of performances with increased playback speed, its effect in discriminating skill levels, and whether this increase is related to the evaluator consciously being aware that the video is being manually manipulated. Methods: A set of ten peg transfer videos (5 novices, 5 experts), were used to evaluate the perceived technical skill of the performers at each video playback speed used (0.4x-3.6x). Objective metrics used for measuring technical skill were also computed for comparison by manipulating the corresponding kinematic data of each performance. Two videos of an expert and novice performing dry lab laparoscopic trials of peg transfer tasks were used to obtain evaluations at each playback speed (0.2x-3.0x) of perception of whether a video is played at real-time playback speed or not. Results: We found that while both novices and experts had increased perceived technical skill as the video playback was increased, the amount of increase was significantly greater for experts. Each increase in the playback speed by 0.4x was associated with, on average, a 0.72-point increase in the GOALS score (95% CI: 0.60-0.84 point increase; p < 0.001) for expert videos and only a 0.24-point increase in the GOALS score (95% CI: 0.13-0.36 point increase; p < 0.001) for novice videos. Conclusion: Due to the differential increase in perceived technical skill due to increased playback speed for experts, the difference between novice and expert skill levels of surgi-

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Validation of the AUA BLUS Tasks

Abstract: BLUS skill task performance scoring can discriminate among basic laparoscopic technical skill levels. Self-reported demographics are an unreliable source of determining laparoscopic technical skill.

Cited by 26 publications

References 14 publications

Performance Improvement (Pi) score: an algorithm to score Pi objectively during E‐BLUS hands‐on training sessions. A European Association of Urology, Section of Uro‐Technology (ESUT) project

Performance Improvement (Pi) score: an algorithm to score Pi objectively during E‐BLUS hands‐on training sessions. A European Association of Urology, Section of Uro‐Technology (ESUT) project

Learning curve of laparoscopic nephrectomy: a prospective pilot study

The effect of video playback speed on surgeon technical skill perception

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