Bugdadi Abdulgadir, Sawaya Robin, Olwi Duaa, Al-Zhrani Gmaan, Azarnoush Hamed, Sabbagh Abdulrahman Jafar, Alsideiri Ghusn, Bajunaid Khalid, Alotaibi Fahad E, Winkler-Schwartz Alexander, Del Maestro Rolando
Department of Neurosurgery and Neurology, Neurosurgical Simulation Research and Training Centre, Montreal Neurologic Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Surgery, Faculty of Medicine,Umm Al-Qura University, Makkah Almukarramah, Saudi Arabia.
Department of Neurosurgery and Neurology, Neurosurgical Simulation Research and Training Centre, Montreal Neurologic Institute and Hospital, McGill University, Montreal, Quebec, Canada.
J Surg Educ. 2018 Jan-Feb;75(1):104-115. doi: 10.1016/j.jsurg.2017.06.018. Epub 2017 Jul 3.
The Fitts and Posner model of motor learning hypothesized that with deliberate practice, learners progress through stages to an autonomous phase of motor ability. To test this model, we assessed the automaticity of neurosurgeons, senior residents, and junior residents when operating on 2 identical tumors using the NeuroVR virtual reality simulation platform.
Participants resected 9 identical simulated tumors on 2 occasions (total = 18 resections). These resections were separated by the removal of a variable number of tumors with different visual and haptic complexities to mirror neurosurgical practice. Consistency of force application was used as a metric to assess automaticity and was defined as applying forces 1 standard deviation above or below a specific mean force application. Amount and specific location of force application during second identical tumor resection was compared to that used for the initial tumor.
This study was conducted at the McGill Neurosurgical Simulation Research and Training Center, Montreal Neurologic Institute and Hospital, Montreal, Canada.
Nine neurosurgeons, 10 senior residents, and 8 junior residents.
Neurosurgeons display statistically significant increased consistency of force application when compared to resident groups when results from all tumor resections were assessed. Assessing individual tumor types demonstrates significant differences between the neurosurgeon and resident groups when resecting hard stiffness similar-to-background (white) tumors and medium-stiffness tumors. No statistical difference in consistency of force application was found when junior and senior residents were compared.
"Experts" display significantly more automaticity when operating on identical simulated tumors separated by a series of different tumors using the NeuroVR platform. These results support the Fitts and Posner model of motor learning and are consistent with the concept that automaticity improves after completing residency training. The potential educational application of our findings is outlined related to neurosurgical resident training.
菲茨和波斯纳的运动学习模型假设，通过刻意练习，学习者会经历不同阶段，最终进入运动能力的自主阶段。为了验证该模型，我们使用NeuroVR虚拟现实模拟平台，评估了神经外科医生、高年资住院医师和低年资住院医师在切除2个相同肿瘤时的自动化程度。
参与者分两次切除9个相同的模拟肿瘤（共18次切除）。这些切除操作之间穿插着切除数量不等、视觉和触觉复杂度不同的肿瘤，以模拟神经外科手术实践。用力的一致性被用作评估自动化程度的指标，定义为施加的力在特定平均用力的1个标准差以上或以下。将第二次切除相同肿瘤时的用力大小和具体位置与首次肿瘤切除时进行比较。
本研究在加拿大蒙特利尔市蒙特利尔神经学研究所和医院的麦吉尔神经外科模拟研究与培训中心进行。
9名神经外科医生、10名高年资住院医师和8名低年资住院医师。
当评估所有肿瘤切除的结果时，与住院医师组相比，神经外科医生在用力一致性方面有统计学上的显著提高。对单个肿瘤类型进行评估时，在切除硬度与背景相似（白色）的硬肿瘤和中等硬度肿瘤时，神经外科医生和住院医师组之间存在显著差异。比较低年资和高年资住院医师时，未发现用力一致性有统计学差异。
在使用NeuroVR平台对被一系列不同肿瘤隔开的相同模拟肿瘤进行手术时，“专家”表现出明显更高的自动化程度。这些结果支持了菲茨和波斯纳的运动学习模型，并且与完成住院医师培训后自动化程度会提高的概念相一致。我们概述了研究结果在神经外科住院医师培训方面的潜在教育应用。
