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The Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework was developed to provide a set of recommendations to help standardize how machine learning studies in urology are reported. This framework serves three purposes: (1) to promote high-quality studies and streamline the peer review process; (2) to enhance reproducibility, comparability, and interpretability of results; and (3) to improve engagement and literacy of machine learning within the urological community.

Background Simulation is becoming more important in the teaching and assessment of technical skills. The purpose of this study was to refine the use of motion analysis parameters (MAPs) to assess performance of a defined task in low-cost pediatric laparoscopic simulators of differing size. Methods 105 participants performed a defined intracorporeal suturing task in large and small pediatric laparoscopic simulators. Outcomes included MAPs — path length, extreme velocity events, and extreme acceleration events in all available degrees of freedom for novices, intermediates, and experts. ANOVA p Results In the smaller simulator, all MAPs discriminated between expertise groups in all degrees of freedom. In the larger simulator, all but one MAP discriminated between expertise groups. Experts demonstrated the greatest variability in performance between the larger and smaller simulators. Conclusion Analysis of motion in the performance of a defined intracorporeal suturing task allowed discrimination between novices, intermediates, and experts in large and small low-cost pediatric laparoscopic simulators. Further refinement in MAPs will determine their role in surgical education. Level of evidence Not applicable.

Pediatric surgical education has traditionally focused on the hard cognitive and psychomotor (technical) skills. While more and more attention is being paid to softer skills such as communication, collaboration, leadership, health advocacy, professionalism and scholarship, the bulk of curricula remain focused on the hard skills. An intricate part of education is the assessment of performance. This article reviews the current literature on the assessment of cognitive and psychomotor skills, focusing particularly on the assessment of technical skills in the realm of simulation.

Although a validated simulator exists for adult laparoscopy, there is no pediatric counterpart. The objective of this study is to develop and validate a pediatric laparoscopic surgery (PLS) simulator.A PLS simulator was developed. Participants were stratified according to level of expertise and tested on the fundamentals of laparoscopic surgery (FLS) and PLS simulators. A subsequent group was tested exclusively on the PLS simulator.The PLS intracorporeal suturing score was lower than its adult counterpart (P = .02). The PLS pattern-cutting score was higher than in the FLS simulator (P.001). If the latter was eliminated from the calculation, the revised total FLS score was significantly better than the revised PLS score. When all participants were combined, total PLS scores as well as performance on 3 of 5 tasks allowed differentiation between novice, intermediate, and expert.The PLS simulator was able to discriminate between the novice, intermediate, and expert using the total PLS score and the performance on 3 of the 5 tasks, thus providing evidence for construct validity. The other 2 tasks will require formal modification or a change in the scoring metrics to establish their independent construct validity.