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Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models

Sivaram Emani, Akshay Swaminathan, Ben Grobman, Julia B. Duvall, Ivan Lopez, Omar Arnaout, and Kevin T. Huang

OBJECTIVE

Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work.

METHODS

The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment.

RESULTS

The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature.

CONCLUSIONS

ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

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Scoping review on the state of racial disparities literature in the treatment of neurosurgical disease: a call for action

Edwin Owolo, Andreas Seas, Brandon Bishop, Jacob Sperber, Zoey Petitt, Alissa Arango, Seeley Yoo, Sharrieff Shah, Julia B. Duvall, Eli Johnson, Nancy Abu-Bonsrah, Samantha Kaplan, Sonia Eden, William W. Ashley Jr., Theresa Williamson, and C. Rory Goodwin

OBJECTIVE

Racial disparities are ubiquitous across medicine in the US. This study aims to assess the evidence of racial disparities within neurosurgery and across its subspecialties, with a specific goal of quantifying the distribution of articles devoted to either identifying, understanding, or reducing disparities.

METHODS

The authors searched the MEDLINE, EMBASE, and Scopus databases by using keywords to represent the concepts of neurosurgery, patients, racial disparities, and specific study types. Two independent reviewers screened the article titles and abstracts for relevance. A third reviewer resolved conflicts. Data were then extracted from the included articles and each article was categorized into one of three phases: identifying, understanding, or reducing disparities. This review was conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines.

RESULTS

Three hundred seventy-one studies published between 1985 and 2023 were included. The distribution of racial disparities literature was not equally spread among specialties, with spine representing approximately 48.3% of the literature, followed by tumor (22.1%) and general neurosurgery (12.9%). Most studies were dedicated to identifying racial disparities (83.6%). The proportion of literature devoted to understanding and reducing disparities was much lower (15.1% and 1.3%, respectively). Black patients were the most negatively impacted racial/ethnic group in the review (63.3%). The Hispanic or Latino ethnic group was the second most negatively impacted (25.1%). The following categories—other outcomes (28.0%), the offering of treatment (21.6%), complications (18.6%), and survival (16.7%)—represented the most frequently measured outcomes.

CONCLUSIONS

Although strides have been taken to identify racial disparities within neurosurgery, fewer studies have focused on understanding and reducing these disparities. The tremendous rise of literature within this domain but the relative paucity of solutions necessitates the study of targeted interventions to provide equitable care for all patients undergoing neurosurgical treatment.