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Challenges to the Future of a Robust Physician Workforce in the United States.

N Engl J Med

January 2025

From the Harvard Kennedy School of Government, Harvard University, Cambridge, MA (R.P.W.); Harvard Business School, Harvard University, Cambridge, MA (R.P.W.); and the Department of Health Law, Policy, and Management, Boston University School of Public Health, Boston (N.C.M.).

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Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.

Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.

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