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John M. Caridi

Researcher at Icahn School of Medicine at Mount Sinai

Publications -  107
Citations -  1376

John M. Caridi is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Retrospective cohort study & Medicine. The author has an hindex of 16, co-authored 95 publications receiving 782 citations. Previous affiliations of John M. Caridi include Mount Sinai Health System & Mount Sinai Hospital.

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Frailty Index Is a Significant Predictor of Complications and Mortality After Surgery for Adult Spinal Deformity.

TL;DR: Preoperative assessment of the mFI in this patient population can be utilized to improve current risk models and was an independent predictor of postoperative complications, mortality, and reoperation in patients undergoing surgery for ASD.
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Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion.

TL;DR: Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.
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Predicting Trends in Cervical Spinal Surgery in the United States from 2020 to 2040.

TL;DR: As expected, large growth in cervical spine surgical volumes is likely to be seen, which could indicate a need for increased numbers of spinal neurosurgeons and orthopedic surgeons.
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Utility of the Hospital Frailty Risk Score for Predicting Adverse Outcomes in Degenerative Spine Surgery Cohorts

TL;DR: Hospital Frailty Risk Score is a better predictor of length of stay (LOS), ICU stays, and nonhome discharges than readmission and may improve on modified frailty index in predicting LOS.
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Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning.

TL;DR: Train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD) and find algorithms outperform ASA scoring for predicting individual risk prognosis.