Global Perspectives on Task Shifting and Task Sharing in Neurosurgery.

BACKGROUND: Neurosurgical task shifting and task sharing (TS/S), delegating clinical care to non-neurosurgeons, is ongoing in many hospital systems in which neurosurgeons are scarce. Although TS/S can increase access to treatment, it remains highly controversial. This survey investigated perceptions of neurosurgical TS/S to elucidate whether it is a permissible temporary solution to the global workforce deficit. METHODS: The survey was distributed to a convenience sample of individuals providing neurosurgical care. A digital survey link was distributed through electronic mailing lists of continental neurosurgical societies and various collectives, conference announcements, and social media platforms (July 2018-January 2019). Data were analyzed by descriptive statistics and univariate regression of Likert Scale scores. RESULTS: Survey respondents represented 105 of 194 World Health Organization member countries (54.1%; 391 respondents, 162 from high-income countries and 229 from low- and middle-income countries [LMICs]). The most agreed on statement was that task sharing is preferred to task shifting. There was broad consensus that both task shifting and task sharing should require competency-based evaluation, standardized training endorsed by governing organizations, and maintenance of certification. When perspectives were stratified by income class, LMICs were significantly more likely to agree that task shifting is professionally disruptive to traditional training, task sharing should be a priority where human resources are scarce, and to call for additional TS/S regulation, such as certification and formal consultation with a neurosurgeon (in person or electronic/telemedicine). CONCLUSIONS: Both LMIC and high-income countries agreed that task sharing should be prioritized over task shifting and that additional recommendations and regulations could enhance care. These data invite future discussions on policy and training programs

Using machine learning to predict mortality and morbidity after Traumatic Brain Injury

A very interesting and important application of machine learning relates to healthcare. There are several studies that illustrate that machines can assist clinicians to make treatment decisions and forecast disease outcomes. In this study, we focus on the setting of Traumatic Brain Injury (TBI). Our goal is to develop machine learning techniques that can accurately predict the capabilities of patients 7 days after hospital admission, in order to support the medical practitioner when deciding specific treatments. We also study the capacity of different input features to predict the outcome, validating the usefulness of innovative biomarkers, such as interleukins, as significant predictors. In our approach, we examine different machine learning models, examining the prediction as a classification problem, aiming to target 3 different capability descriptors (Glasgow Comma Scale, Glasgow Outcome Scale and Karnofsky Performance Scale). The promising first results, reaching an f1-micro score of approximately 80%, indicate that this avenue of machine learning exploitation in the TBI setting can be an important addition to the medical arsenal for decision support. © 2022 ACM

MACHINE LEARNING to DEVELOP A MODEL THAT PREDICTS EARLY IMPENDING SEPSIS in NEUROSURGICAL PATIENTS

Sepsis is currently defined as a "life-threatening organ dysfunction caused by a dysregulated host response to infection". The early detection and prediction of sepsis is a challenging task, with significant potential gains regarding the lives of patient and - as such - should be researched comprehensively. The main goal of this study is to take anonymised and appropriately processed data in order to detect infections which imply future probability for sepsis. In that way, medical practitioners may have the opportunity to treat patient appropriately in a proactive manner. Feature selection techniques were applied in order to define the most important features to feed machine learning models and maximize the performance of the prediction as a binary classification problem. We also aim to highlight the relation of specific clinical input features to the prediction outcome, possibly contributing to an improved, data-driven understanding of this multi-factorial dysfunction. Early findings indicating promising classification performance, with different machine learning algorithms, but also based on appropriate feature engineering, building upon features with a time-sensitive aspect (i.e. features representing different samplings in different positions in time). © 2022 ACM