Chiari malformation with concurrent papilledema: a patient-level meta-analysis of case reports and case series by Alejandro Pando, Daniel J. Valdivia, Daniel A. Brenner, Mohammed Ali Alvi, Hai Sun, James K. Liu, Ira Goldstein

Get full text

Is information provided within chronic subdural haematoma education resources adequate? A scoping review. by Conor S Gillespie, Samuel Khanna, Mark E Vivian, Samuel McKoy, Alvaro Yanez Touzet, Ellie Edlmann, Daniel J Stubbs, Benjamin M Davies

“…However, its occurrence in frail populations, remote from specialist neurosurgeons who currently triage treatment decisions, challenges this. …”
Get full text

Introducing an index on prediction of post-revascularization cerebral infarction using preoperative CT perfusion parameters in moyamoya disease by Xiaojun Hao, Chao Zhang, Chen Yang, Xintong Zhao, Yunfeng Zhou, Juan Wang

“…The risk of post-CI can be predicted using preoperative CTP parameters, which will assist neurosurgeons with surgical decisions and implementing individualized prophylactic strategies. …”
Get full text

The Morphological Stenosis Pattern of the Caroticoclinoid Foramen by Ioannis Paschopoulos, George Triantafyllou, Panagiotis Papadopoulos-Manolarakis, Sabino Luzzi, Nektaria Karangeli, George Tsakotos, Renato Galzio, Maria Piagkou

“…In 16.8% of the current sample with CCFs (irrespective of their morphology), we observed that the ICA is probably at a high risk of compression. Radiologists and neurosurgeons intervening in the area should preoperatively diagnose the possibility of ICA compression in this area.…”
Get full text

Role of Virtual iMRI in Glioblastoma Surgery: Advantages, Limitations, and Correlation with iCT and Brain Shift by Erica Grasso, Francesco Certo, Mario Ganau, Giulio Bonomo, Giuseppa Fiumanò, Giovanni Buscema, Andrea Maugeri, Antonella Agodi, Giuseppe M. V. Barbagallo

“…Its integration within a multimodal imaging protocol may provide neurosurgeons with intraoperatively updated imaging.…”
Get full text

A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults by Daisu Abe, Motoki Inaji, Takeshi Hase, Eiichi Suehiro, Naoto Shiomi, Hiroshi Yatsushige, Shin Hirota, Shu Hasegawa, Hiroshi Karibe, Akihiro Miyata, Kenya Kawakita, Kohei Haji, Hideo Aihara, Shoji Yokobori, Takeshi Maeda, Takahiro Onuki, Kotaro Oshio, Nobukazu Komoribayashi, Michiyasu Suzuki, Taketoshi Maehara

“…At the cutoff value from the Youden index, the model showed a sensitivity, specificity, and positive predictive value of 60, 96, and 38%, respectively. When neurosurgeons attempted to predict neurological deterioration using the same testing data, their values were 20, 94, and 19%, respectively.ConclusionIn this study, our predictive model showed an acceptable performance in detecting neurological deterioration after mild TBI. …”
Get full text