Neurosurg Focus
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OBJECTIVEPapers from 2002 to 2017 have highlighted consistent unique socioeconomic challenges and opportunities facing military neurosurgeons. Here, the authors focus on the reserve military neurosurgeon who carries the dual mission of both civilian and military responsibilities. METHODSSurvey solicitation of current active duty and reserve military neurosurgeons was performed in conjunction with the AANS/CNS Joint Committee of Military Neurosurgeons and the Council of State Neurosurgical Societies. ⋯ What remains astonishing is that 91.7% of those reserve neurosurgeons who were deployed noted the experience to be rewarding despite seeing a 20% reduction in income, on average, during the fiscal year of a 6-month deployment. CONCLUSIONSReserve neurosurgeons are satisfied with their military service while making substantial contributions to the military's neurosurgical capabilities, with the overwhelming majority of current military reservists having been deployed or mobilized during their reserve commitments. Through the authors' modeling, the impact of deployment on the military neurosurgeon, neurosurgeon's practice, and the local community can be significantly mitigated by a larger practice environment.
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OBJECTIVEIn combat and austere environments, evacuation to a location with neurosurgery capability is challenging. A planning target in terms of time to neurosurgery is paramount to inform prepositioning of neurosurgical and transport resources to support a population at risk. This study sought to examine the association of wait time to craniectomy with mortality in patients with severe combat-related brain injury who received decompressive craniectomy. ⋯ CONCLUSIONSPostoperative mortality was significantly lower when craniectomy was initiated within 5.33 hours of injury. Further research to optimize craniectomy timing and mitigate delays is needed. Functional outcomes should also be evaluated.
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On a Sunday morning at 06:22 on October 23, 1983, in Beirut, Lebanon, a semitrailer filled with TNT sped through the guarded barrier into the ground floor of the Civilian Aviation Authority and exploded, killing and wounding US Marines from the 1st Battalion 8th Regiment (2nd Division), as well as the battalion surgeon and deployed corpsmen. The truck bomb explosion, estimated to be the equivalent of 21,000 lbs of TNT, and regarded as the largest nonnuclear explosion since World War II, caused what was then the most lethal single-day death toll for the US Marine Corps since the Battle of Iwo Jima in World War II. Considerable neurological injury resulted from the bombing. ⋯ Training of nurses, corpsmen, and also nonmedical personnel is essential. In a large-scale evolution, nonmedical personnel may monitor vital signs, work as scribes or stretcher bearers, and run messages. It is incumbent upon medical providers and neurosurgeons in particular to be aware of the potential for mass casualty events and to make necessary preparations.
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OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. ⋯ The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing's disease. CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.
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OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. ⋯ RESULTSThe authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895). CONCLUSIONSML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.