Objective: Increased restrictions on working hours and the resultant decrease in theater time coupled with greater scrutiny to demonstrate proficiency at surgical tasks has resulted in the incorporation of simulators for surgical training. This literature review describes the use of cadaveric simulators in postgraduate neurosurgical training, with the aim to analyze their effectiveness in improving surgical performance.
Methods: An electronic literature search of the MEDLINE, Embase, and Cochrane Library databases was conducted to identify studies that look at the efficacy of cadaveric simulation in neurosurgical training. Studies that were eligible were those that assessed either objectively or subjectively the effectiveness of human cadaver models in cranial or spinal neurosurgical training. Studies that did not assess efficacy on training, looked at animal cadavers, or noncadaveric simulators were excluded.
Results: Twelve studies were deemed to meet the eligibility criteria. Only 4 of the studies used objective measures to assess the effectiveness of cadaveric simulators on training. Most studies reported a positive impact of cadaveric simulators on training.
Conclusions: Most studies identified in this review failed to provide strong objective evidence for effectiveness in achieving competency and good outcomes in the theatres. Lack of use of validated skills assessment tools prevented studies from associating cadaveric training with improvement in operating skills. Future studies should aim to address these shortcomings and focus on validating cadaveric simulation, ensuring only those that improve performance of both technical and nontechnical skills are pursued.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.wneu.2018.07.015 | DOI Listing |
J Neurosurg
January 2025
Departments of1Neurosurgery.
Objective: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary.
View Article and Find Full Text PDFJ Neurosurg
January 2025
Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran University of Medical Sciences, Tehran, Iran.
J Med Syst
January 2025
Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, PA, USA.
Large language models (LLMs) have been utilized to automate tasks like writing discharge summaries and operative reports in neurosurgery. The present study evaluates their ability to identify current procedural terminology (CPT) codes from operative reports. Three LLMs (ChatGPT 4.
View Article and Find Full Text PDFBackground: During last ten years, we have developed a digital library with educational materials in Physical medicine and rehabilitation.
Objectives: The objective of current article is the preparation of an electronic library with educational materials in the area of physical medicine, physical therapy and rehabilitation, and the comparative evaluation of the impact of this repository on the quality of education of students and trainees in the field.
Methodology: The electronic library includes e-books on different topics, elements of the specialty "Physical and rehabilitation medicine (PRM)" or Physiatry - with theoretical data, practical issues and case reports with videos of real patients.
Neurosurg Rev
January 2025
Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!