OBJECTIVE: The landscape of surgical training is undergoing transformative changes, especially in the realm of robot-assisted procedures like radical prostatectomy (RARP). This narrative review explores the evolving methodologies and innovations in RARP training, emphasizing the shift from traditional training approaches, such as the Halsted method, to more scientific methods like proficiency-based progression (PBP). The rationale for the review stems from the increased adoption of robot-assisted surgery and the resulting increase in associated adverse events reported in the United States. The Patient Safety in Robotic Surgery (SAFROS) project initiated by the European Commission of the World Health Organization emphasized the importance of structured training programs for robotic surgeons. However, the review points out the limited availability of standardized curricula for RARP training, leading to non-homogeneous training worldwide.
METHODS: PubMed was searched primarily for the following topics: training AND robotic AND prostatectomy; robotic training AND prostatectomy AND learning; simulator AND robotic AND prostatectomy. Literature was selected based on historical significance and landmark studies as well as publications published after 2000. References from select studies were additionally included.
UNASSIGNED: The advent of robotic surgery, especially in RARP, demands unique skills necessitating specialized training. The review delves into the diverse stages of robotic surgery training, starting with e-learning and progressing through virtual reality simulators, dry and wet laboratories, culminating in modular console training. Each training stage plays a critical role, addressing the challenges posed by new technologies and tools.
CONCLUSIONS: The ever-evolving landscape of surgical training underscores the critical need for globally standardized, effective, and accessible programs. PBP emerges as a promising methodology, and technological advancements open new possibilities for telementoring via platforms like 5G. This review emphasizes the imperative to equip surgeons with the requisite skills for intricate procedures like RARP, addressing current challenges while anticipating the future developments in this dynamic field.

Anticipatory brain activity makes it possible to predict the occurrence of expected situations. However, events such as traffic accidents are statistically unpredictable and can generate catastrophic consequences. This study investigates the brain activity and effective connectivity associated with anticipating and processing such unexpected, unavoidable accidents. We asked 161 participants to ride a motorcycle simulator while recording their electroencephalographic activity. Of these, 90 participants experienced at least one accident while driving. We conducted both within-subjects and between-subjects comparisons. During the pre-accident period, the right inferior parietal lobe (IPL), left anterior cingulate cortex (ACC), and right insula showed higher activity in the accident condition. In the post-accident period, the bilateral orbitofrontal cortex, right IPL, bilateral ACC, and middle and superior frontal gyrus also showed increased activity in the accident condition. We observed greater effective connectivity within the nodes of the limbic network (LN) and between the nodes of the attentional networks in the pre-accident period. In the post-accident period, we also observed greater effective connectivity between networks, from the ventral attention network (VAN) to the somatomotor network and from nodes in the visual network, VAN, and default mode network to nodes in the frontoparietal network, LN, and attentional networks. This suggests that activating salience-related processes and emotional processing allows the anticipation of accidents. Once an accident has occurred, integration and valuation of the new information takes place, and control processes are initiated to adapt behavior to the new demands of the environment.

UNASSIGNED: The spatial accuracy of microsurgical manipulations is one of the critical factors in successful surgical interventions. The purpose of this study was to create a low-cost, high-fidelity, and easy-to-use simulator for microsurgical skills training, which can be made by residents themselves at home.
UNASSIGNED: In response to the COVID-19 pandemic, we created a device for spatial accuracy microsurgical skills training and implemented it in our resident\'s training program. We propose a design for basic and advanced models. The simulator consisted of commonly available products.
UNASSIGNED: A low-cost, durable, and high-fidelity basic model has been developed at a total cost of <10 dollars per unit. The model allows trainees to practice the critical microsurgical skills of tool targeting in a home-based setting.
UNASSIGNED: The developed device can be assembled at an affordable price using commercially available materials. Such simulation models can provide valuable training opportunities for microsurgery residents.

OBJECTIVE: Minimally invasive surgery for gastrointestinal cancers is rapidly advancing; therefore, surgical education must be changed. This study aimed to examine the feasibility of early initiation of robotic surgery education for surgical residents.
METHODS: The ability of staff physicians and residents to handle robotic surgical instruments was assessed using the da Vinci® skills simulator (DVSS). The short-term outcomes of 32 patients with colon cancer who underwent robot-assisted colectomy (RAC) by staff physicians and residents, supervised by a dual console system, between August 2022 and March 2024 were compared.
RESULTS: The performances of four basic exercises were assessed after implementation of the DVSS. Residents required less time to complete these exercises and achieved a higher overall score than staff physicians. There were no significant differences in the short-term outcomes, operative time, blood loss, incidence of postoperative complications, and length of the postoperative hospital stay of the two surgeon groups.
CONCLUSIONS: Based on the evaluation involving the DVSS and RAC results, it appears feasible to begin robotic surgery training at an early stage of surgical education using a dual console system.

BACKGROUND: Non-ophthalmologists often lack sufficient operational training to use a direct ophthalmoscope proficiently, resulting in a global deficit of basic ophthalmological skills among general practitioners. This deficiency hampers the timely diagnosis, referral, and intervention of patients. Consequently, the optimization of teaching tools and methods to enhance teaching efficiency is imperative. This study explores the effectiveness of the Eyesi Direct Ophthalmoscope Simulator (Eyesi) as an innovative tool for fundus examination training.
METHODS: Medical undergraduates were randomly assigned to Group A or B (n = 168). All participants completed a pre-training questionnaire. Group A received Eyesi training, while Group B underwent traditional direct ophthalmoscope (TDO) training. Subsequently, participants answered questionnaires relevant to their respective training methods. Both groups exchanged training tools and completed a summary questionnaire.
RESULTS: After training, 54.17% of participants believed that images presented by the Eyesi were consistent with the real fundus. Group A scored significantly higher than Group B in fundus structure recognition and self-confidence in examination. The degree of mastery over fundus theory score increased from 6.10 ± 0.13 to 7.74 ± 0.16 (P < 0.001) in Group A, but Group B did not demonstrate a significant difference. We also compared undergraduates\' tendencies for different learning purposes, 75.59% of participants preferred the Eyesi to TDO as a training tool, and 88.41% of participants were receptive to introducing the Eyesi in training.
CONCLUSIONS: According to subjective participant feedback, Eyesi outperformed TDO in fundus observation, operational practice, and theoretical learning. It effectively equips undergraduates with fundus examination skills, potentially promoting the use of direct ophthalmoscopes in primary medical institutions.

UNASSIGNED: Suction-assisted laryngoscopy and airway decontamination (SALAD) is a new modality and training manikins are quite costly. Few modifications have been described with their pluses and minuses. We describe a low-cost simulator that replicates fluid contamination of the airway at various flow rates and allows the practice of SALAD in vitro.
UNASSIGNED: We modified a standard Laerdal airway management trainer with locally available equipment to simulate varying rates of continuous vomiting or hemorrhage into the airway during intubation. The effectiveness of our SALAD simulator was tested during an advanced airway workshop of the Airway Management Foundation (AMF). The workshop had a brief common presentation on the learning objective of the SALAD technique followed by a demonstration to small groups of 5-6 participants at one time with necessary instructions. This was followed by a hands-on practical learning session on the simulator.
UNASSIGNED: One hundred and five learners used the simulator including 15 faculties and 90 participants (48 on ICU and 42 on ENT workstations). At the end of the session, the workshop faculty and participants were asked to rate their level of confidence in managing similar situations in real practice on a four-point Likert scale. All 15 faculty members and 70 out of 90 participants felt very confident in managing similar situations in real practice. Fifteen participants felt fairly confident and 5 felt slightly confident.
UNASSIGNED: In resource-limited settings, our low-cost SALAD simulator is a good educational tool for training airway managers in the skills of managing continuously and rapidly soiling airways.
UNASSIGNED: Kumar R, Kumar R. An Indigenous Suction-assisted Laryngoscopy and Airway Decontamination Simulation System. Indian J Crit Care Med 2024;28(7):702-705.

OBJECTIVE: This study proposes a system that can simulate head radiography by combining a technique for estimating human posture from moving images (hereafter referred to as \"pose estimation technique\") and use of two cameras capable of acquiring RGB images to determine body position during positioning.
METHODS: The angles of the median sagittal plane (MS), axial plane (AX), and orbitomeatal baseline (OM) were obtained using the pose estimation technique from frontal and lateral images captured after positioning. The resulting radiographs were displayed according to the results.
RESULTS: The head tilt during positioning could be determined based on the coordinate data of feature points acquired using the pose estimation technique. In an imaging experiment using a simulated human patient, errors increased as head tilt increased; however, the mean error values in each axis were 0.9° for MS, 0.8° for AX, and 1.5°for OM, when the patient was correctly positioned.
CONCLUSIONS: The pose estimation technique can assist in evaluating positioning accuracy in radiography and is expected to be used as a potential simulator system.

[This corrects the article DOI: 10.3389/fphys.2023.1292523.].

OBJECTIVE: To assess attempts to proficiency of experienced veterinary surgeons for 2 surgical tasks when using a robotic simulator (Mimic dV-Trainer; Surgical Sciences) and determine factors associated with the successful performance of these tasks.
METHODS: Veterinary surgeons with rigid, minimally invasive surgery experience performed 2 tasks (\"pick and place\" and \"knot the ring 1\") using the simulator until they attained proficiency. Individual performance variables were recorded. The number of attempts to proficiency was recorded. Performance variables were also assessed for effect on proficiency by the Kendall tau correlation and hierarchical multiple linear regression. The study period was from July 25, 2022, through December 14, 2022.
RESULTS: The 18 surgeons enrolled required a median of 8.5 attempts (95% CI, 7 to 12; range, 6 to 22) to reach proficiency for the basic task versus 27 attempts (95% CI, 21 to 38; range, 10 to 63) for the advanced task. Surgeons took a median of 6 minutes (range, 3 to 11 minutes) to complete training for the basic task and 12 minutes (range, 4 to 46 minutes) for the advanced task. The number of attempts to reach proficiency correlated strongly with economy of motion (tau = 0.72), instrument collisions (tau = 0.72), and time to completion (tau = 0.96).
CONCLUSIONS: Although experienced surgeons required a high number of attempts to gain proficiency in robotic simulator tasks, they did achieve proficiency quickly, encouraging future investigations into their use for training. Specific motion metrics were identified which improved efficiency during training.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.