Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis-Reference-Cited by-同舟云学术

Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis

Published:2024-05-03 Issue:5 Volume:12 Page:1477-1483
ISSN:2212-134X
Container-title:Spine Deformity
language:en
Short-container-title:Spine Deform

Author:

Fields Michael W.,Zaifman Jay,Malka Matan S.^ORCID,Lee Nathan J.,Rymond Christina C.,Simhon Matthew E.,Quan Theodore,Roye Benjamin D.,Vitale Michael G.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s43390-024-00889-w.pdf

Reference39 articles.

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2. Whitcomb WF, Lagu T, Krushell RJ et al (2015) Experience with designing and implementing a bundled payment program for total hip replacement. Jt Comm J Qual Patient Saf 41(9):406–413. https://doi.org/10.1016/s1553-7250(15)41052-9

3. Gulotta LV, Padgett DE, Sculco TP, Urban M, Lyman S, Nestor BJ (2011) Fast track THR: one hospital’s experience with a 2-day length of stay protocol for total hip replacement. HSS J 7(3):223–228. https://doi.org/10.1007/s11420-011-9207-2

4. Somani S, Di CJ, Kim JS et al (2017) ASA classification as a risk stratification tool in adult spinal deformity surgery: a study of 5805 patients. Glob Spine J 7(8):719–726. https://doi.org/10.1177/2192568217700106

5. Matsumoto H, Larson EL, Warren SI et al (2022) A clinical risk model for surgical site infection following pediatric spine deformity surgery. J Bone Jt Surg Am 104(4):364–375. https://doi.org/10.2106/JBJS.21.00751