Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale
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Title
Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale
Authors
Keywords
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Journal
NEUROSURGERY
Volume 91, Issue 2, Pages 322-330
Publisher
Ovid Technologies (Wolters Kluwer Health)
Online
2022-06-15
DOI
10.1227/neu.0000000000001999
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- (2019) Katherine E. Pierce et al. JOURNAL OF CLINICAL NEUROSCIENCE
- Enhanced recovery after surgery (ERAS) for spine surgery: A systematic review.
- (2019) Nicholas Dietz et al. World Neurosurgery
- Predicting extended operative time and length of inpatient stay in cervical deformity corrective surgery
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- Key challenges for delivering clinical impact with artificial intelligence
- (2019) Christopher J. Kelly et al. BMC Medicine
- Trends in resource utilization and rate of cervical disc arthroplasty and anterior cervical discectomy and fusion throughout the United States from 2006 to 2013
- (2018) Comron Saifi et al. Spine Journal
- Extended Length of Stay in Elderly Patients after Anterior Cervical Discectomy and Fusion Is Not Attributable to Baseline Illness Severity or Postoperative Complications
- (2018) Owoicho Adogwa et al. World Neurosurgery
- Predictors of Hospital Length of Stay and 30-Day Readmission in Cervical Spondylotic Myelopathy Patients: An Analysis of 3057 Patients Using the ACS-NSQIP Database
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- Predictors of Nonneurologic Complications and Increased Length of Stay After Cervical Spine Osteotomy
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- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
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- Bundled Payment Models in Spine Surgery: Current Challenges and Opportunities, a Systematic Review
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- United States Health Care Reform
- (2016) Barack Obama JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
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