Stulberg, J. J. et al. Affiliation between surgeon technical abilities and affected person outcomes. JAMA Surg. 155, 960 (2020).
Google Scholar
Ng, R., Chahine, S., Lanting, B. & Howard, J. Unpacking the literature on stress and resiliency: A story overview centered on learners within the working room. J. Surg. Educ. 76(2), 343–353 (2019).
Google Scholar
Ahsani-Estahbanati, E., Doshmangir, L., Najafi, B., Akbari Sari, A. & Sergeevich, G. V. Incidence fee and monetary burden of medical errors and coverage interventions to handle them: A multi-method examine protocol. Well being Serv. Outcomes Res. Methodol. 22(2), 244–252 (2022).
Google Scholar
Drexler, R. et al. Affiliation of the classification of intraoperative hostile occasions (ClassIntra) with problems and neurological consequence after neurosurgical procedures: a potential cohort examine. Acta Neurochir. (Wien) 165, 2015 (2023).
Google Scholar
Martin, J. A. et al. Goal structured evaluation of technical talent (OSATS) for surgical residents. Br. J. Surg. 84(2), 273–278 (1997).
Google Scholar
Khan, M. R., Begum, S. Apprenticeship to simulation-The metamorphosis of surgical coaching. The
Journal of the Pakistan Medical Affiliation. 71, S72–S76. (2021).
Haluck, R. S. & Krummel, T. M. Computer systems and digital actuality for surgical training within the twenty first century. Arch. Surg. 135(7), 786–792 (2000).
Google Scholar
Vanessa, N. P. & Teodor, P. G. Simulation in surgical training. Can. Med. Assoc. J. 182(11), 1191 (2010).
Google Scholar
Gélinas-Phaneuf, N. & Del Maestro, R. F. Surgical experience in neurosurgery: Integrating idea into follow. Neurosurgery 73(Suppl_1), S30–S38 (2013).
Google Scholar
Brightwell, A. & Grant, J. Competency-based coaching: Who advantages?. Postgrad. Med. J. 89(1048), 107 (2013).
Google Scholar
Portelli, M., Bianco, S. F., Bezzina, T. & Abela, J. E. Digital actuality coaching in contrast with apprenticeship coaching in laparoscopic surgical procedure: A meta-analysis. Ann. R. Coll. Surg. Engl. 102(9), 672–684 (2020).
Google Scholar
Yilmaz, R. et al. Steady monitoring of surgical bimanual experience utilizing deep neural networks in digital actuality simulation. npj Digit. Med. 5(1), 54 (2022).
Google Scholar
Yilmaz, R., Winkler-Schwartz, A., Mirchi, N., Del Maestro, R. Strategies and methods for steady monitoring of activity efficiency, Worldwide Patent Quantity: WO2022077109A12022.
Delorme, S., Laroche, D., DiRaddo, R. & Del Maestro, R. F. NeuroTouch: A physics-based digital simulator for cranial microneurosurgery coaching. Oper. Neurosurg. 71(suppl_1), ons32–ons42 (2012).
Google Scholar
Sabbagh, A. J. et al. Roadmap for creating advanced digital actuality simulation eventualities: Subpial neurosurgical tumor resection mannequin. World Neurosurg. 139, e220–e229 (2020).
Google Scholar
Fazlollahi, A. M. et al. Impact of synthetic intelligence tutoring vs professional instruction on studying simulated surgical abilities amongst medical college students: A randomized scientific trial. JAMA Netw. Open 5(2), e2149008 (2022).
Google Scholar
Fazlollahi, A. M. et al. AI in surgical curriculum design and unintended outcomes for technical competencies in simulation coaching. JAMA Netw. Open 6(9), e2334658 (2023).
Google Scholar
Juszczak, E., Altman, D. G., Hopewell, S. & Schulz, Okay. Reporting of multi-arm parallel-group randomized trials: Extension of the CONSORT 2010 assertion. JAMA 321(16), 1610–1620 (2019).
Google Scholar
Cheng, A. et al. Reporting pointers for well being care simulation analysis: Extensions to the CONSORT and STROBE statements. Adv. Simul. 1(1), 25 (2016).
Google Scholar
Liu, X. et al. Reporting pointers for scientific trial studies for interventions involving synthetic intelligence: The CONSORT-AI extension. Nat. Med. 26(9), 1364–1374 (2020).
Google Scholar
World Medical Affiliation. World Medical Affiliation Declaration of Helsinki: Moral ideas for medical analysis involving human topics. JAMA 310(20), 2191–2194 (2013).
Google Scholar
calculator.internet. Accessed January 1, 2023. https://www.calculator.internet/ [
Esquenazi, Y. et al. The survival advantage of “Supratotal” resection of Glioblastoma using selective cortical mapping and the subpial technique. Neurosurgery 81(2), 275 (2017).
Google Scholar
Spencer, S. S. et al. Multiple subpial transection for intractable partial epilepsy: An international meta-analysis. Epilepsia 43(2), 141–145 (2002).
Google Scholar
Ledwos, N. et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. J. Neurosurg. 173, 1–12 (2022).
Winkler-Schwartz, A. et al. Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw. Open 2(8), e198363 (2019).
Google Scholar
Yilmaz, R. et al. Nondominant hand skills spatial and psychomotor analysis during a complex virtual reality neurosurgical task-a case series study. Oper. Neurosurg. (Hagerstown). 23(1), 22–30 (2022).
Google Scholar
Natheir, S. et al. Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task. Comput. Biol. Med. 152, 106286 (2023).
Google Scholar
Eppich, W. & Cheng, A. Promoting excellence and reflective learning in simulation (PEARLS): Development and rationale for a blended approach to health care simulation debriefing. Simul. Healthc. 10(2), 106 (2015).
Google Scholar
Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T. & Van Merriënboer, J. J. G. Development of an instrument for measuring different types of cognitive load. Behav. Res. Methods 45(4), 1058–1072 (2013).
Google Scholar
Ward, T. M. et al. Surgical data science and artificial intelligence for surgical education. J. Surg. Oncol. 124(2), 221–230 (2021).
Google Scholar
Winkler-Schwartz, A. et al. Artificial intelligence in medical education: Best practices using machine learning to assess surgical expertise in virtual reality simulation. J. Surg. Educ. 76(6), 1681–1690 (2019).
Google Scholar
Dean, W. H. et al. Intense simulation-based surgical education for manual small-incision cataract surgery: The ophthalmic learning and improvement initiative in cataract surgery randomized clinical trial in Kenya, Tanzania, Uganda, and Zimbabwe. JAMA Ophthalmol. 139(1), 9–15 (2021).
Google Scholar
Ledwos, N. et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. J. Neurosurg. 137(4), 1160–1171 (2022).
Google Scholar
Wang, Z. & Shen, J. Simulation training in spine surgery. J. Am. Acad. Orthop. Surg. 30(9), 400–408 (2022).
Google Scholar
Yari, S. S., Jandhyala, C. K., Sharareh, B., Athiviraham, A. & Shybut, T. B. Efficacy of a virtual arthroscopic simulator for orthopaedic surgery residents by year in training. Orthop. J. Sports Med. 6(11), 2325967118810176 (2018).
Google Scholar
Logishetty, K., Rudran, B. & Cobb, J. P. Virtual reality training improves trainee performance in total hip arthroplasty: A randomized controlled trial. Bone Jt. J. 101-B(12), 1585–1592 (2019).
Google Scholar
Farquharson, A. L., Cresswell, A. C., Beard, J. D. & Chan, P. Randomized trial of the effect of video feedback on the acquisition of surgical skills. Br. J. Surg. 100(11), 1448–1453 (2013).
Google Scholar
Al Fayyadh, M. J. et al. Immediate auditory feedback is superior to other types of feedback for basic surgical skills acquisition. J. Surg. Educ. 74(6), e55–e61 (2017).
Google Scholar
Porte, M. C., Xeroulis, G., Reznick, R. K. & Dubrowski, A. Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am. J. Surg. 193(1), 105–110 (2007).
Google Scholar
Yilmaz, R. et al. Effect of feedback modality on simulated surgical skills learning using automated educational systems—A four-arm randomized control trial. J. Surg. Educ. 81(2), 275–287 (2024).
Google Scholar
Sweller, J. Cognitive load during problem solving: Effects on learning. Cogn. Sci. 12(2), 257–285 (1988).
Google Scholar
Young, J. Q., Van Merrienboer, J., Durning, S. & Ten Cate, O. Cognitive Load Theory: Implications for medical education: AMEE Guide No. 86. Med. Teach. 36(5), 371–384 (2014).
Google Scholar
Sweller, J. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 22(2), 123–138 (2010).
Google Scholar
Mayer, R. E. Cognitive theory of multimedia learning. In The Cambridge Handbook of Multimedia Learning. Cambridge Handbooks in Psychology 2nd edn (ed. Mayer, R. E.) 43–71 (Cambridge University Press, 2014).
Google Scholar
Kasneci, E. et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023).
Google Scholar
Winkler-Schwartz, A. et al. A comparison of visual rating scales and simulated virtual reality metrics in neurosurgical training: A generalizability theory study. World Neurosurg. 127, e230 (2019).
Google Scholar
Kiyasseh, D. et al. A vision transformer for decoding surgeon activity from surgical videos. Nat. Biomed. Eng. 7, 1–17 (2023).
Google Scholar
Kiyasseh, D. et al. Human visual explanations mitigate bias in AI-based assessment of surgeon skills. npj Digit. Med. 6(1), 54 (2023).
Google Scholar
Tran, D. H. et al. Quantitation of tissue resection using a brain tumor model and 7-T magnetic resonance imaging technology. World Neurosurg. 148, e326–e339 (2021).
Google Scholar
Winkler-Schwartz, A. et al. Creating a comprehensive research platform for surgical technique and operative outcome in primary brain tumor neurosurgery. World Neurosurg. 144, e62–e71 (2020).
Google Scholar
Almansouri, A. et al. Continuous instrument tracking in a cerebral corticectomy ex vivo calf brain simulation model: Face and content validation. Oper. Neurosurg. 27, 106 (2024).
Google Scholar
Sawaya, R. et al. Virtual reality tumor resection: The force pyramid approach. Oper. Neurosurg. 14(6), 686–696 (2017).
Google Scholar
Yilmaz, R. SubPialResection101-KFMC_scenario.xml_2015-Oct-22_14h06m26s_log.csv. https://doi.org/10.6084/m9.figshare.15132507.v1. 2021.
