These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

122 related articles for article (PubMed ID: 38797444)

  • 1. Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.
    Pean CA; Buddhiraju A; Shimizu MR; Chen TL; Esposito JG; Kwon YM
    J Arthroplasty; 2024 Nov; 39(11):2824-2830. PubMed ID: 38797444
    [TBL] [Abstract][Full Text] [Related]  

  • 2. The CARDE-B Scoring System Predicts 30-Day Mortality After Revision Total Joint Arthroplasty.
    Raad M; Amin R; Puvanesarajah V; Musharbash F; Rao S; Best MJ; Amanatullah DF
    J Bone Joint Surg Am; 2021 Mar; 103(5):424-431. PubMed ID: 33475307
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.
    Oosterhoff JHF; de Hond AAH; Peters RM; van Steenbergen LN; Sorel JC; Zijlstra WP; Poolman RW; Ring D; Jutte PC; Kerkhoffs GMMJ; Putter H; Steyerberg EW; Doornberg JN;
    Clin Orthop Relat Res; 2024 Aug; 482(8):1472-1482. PubMed ID: 38470976
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?
    El-Galaly A; Grazal C; Kappel A; Nielsen PT; Jensen SL; Forsberg JA
    Clin Orthop Relat Res; 2020 Sep; 478(9):2088-2101. PubMed ID: 32667760
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.
    Abraham VM; Booth G; Geiger P; Balazs GC; Goldman A
    Clin Orthop Relat Res; 2022 Nov; 480(11):2137-2145. PubMed ID: 35767804
    [TBL] [Abstract][Full Text] [Related]  

  • 6. How Accurate Are the Surgical Risk Preoperative Assessment System (SURPAS) Universal Calculators in Total Joint Arthroplasty?
    Trickey AW; Ding Q; Harris AHS
    Clin Orthop Relat Res; 2020 Feb; 478(2):241-251. PubMed ID: 31904684
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Temporal Trends in Revision Total Hip and Knee Arthroplasty from 2008 to 2018: Gaps and Opportunities.
    Siddiqi A; Warren JA; Manrique-Succar J; Molloy RM; Barsoum WK; Piuzzi NS
    J Bone Joint Surg Am; 2021 Jul; 103(14):1335-1354. PubMed ID: 34260441
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty.
    Buddhiraju A; Shimizu MR; Subih MA; Chen TL; Seo HH; Kwon YM
    J Arthroplasty; 2023 Oct; 38(10):1959-1966. PubMed ID: 37315632
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.
    Klemt C; Uzosike AC; Esposito JG; Harvey MJ; Yeo I; Subih M; Kwon YM
    Arch Orthop Trauma Surg; 2023 Apr; 143(4):2235-2245. PubMed ID: 35767040
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.
    Kunze KN; So MM; Padgett DE; Lyman S; MacLean CH; Fontana MA
    Clin Orthop Relat Res; 2023 Sep; 481(9):1745-1759. PubMed ID: 37256278
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Frailty Predicts Medical Complications, Length of Stay, Readmission, and Mortality in Revision Hip and Knee Arthroplasty.
    Traven SA; Reeves RA; Slone HS; Walton ZJ
    J Arthroplasty; 2019 Jul; 34(7):1412-1416. PubMed ID: 30930155
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Is Hypoalbuminemia Associated With Septic Failure and Acute Infection After Revision Total Joint Arthroplasty? A Study of 4517 Patients From the National Surgical Quality Improvement Program.
    Bohl DD; Shen MR; Kayupov E; Cvetanovich GL; Della Valle CJ
    J Arthroplasty; 2016 May; 31(5):963-7. PubMed ID: 26718779
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.
    Buddhiraju A; Shimizu MR; Seo HH; Chen TL; RezazadehSaatlou M; Huang Z; Kwon YM
    Med Biol Eng Comput; 2024 Aug; 62(8):2333-2341. PubMed ID: 38558351
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.
    Chen TL; Shimizu MR; Buddhiraju A; Seo HH; Subih MA; Chen SF; Kwon YM
    Med Biol Eng Comput; 2024 Jul; 62(7):2073-2086. PubMed ID: 38451418
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?
    Fontana MA; Lyman S; Sarker GK; Padgett DE; MacLean CH
    Clin Orthop Relat Res; 2019 Jun; 477(6):1267-1279. PubMed ID: 31094833
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?
    Harris AHS; Kuo AC; Weng Y; Trickey AW; Bowe T; Giori NJ
    Clin Orthop Relat Res; 2019 Feb; 477(2):452-460. PubMed ID: 30624314
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA.
    Kunze KN; Sculco PK; Zhong H; Memtsoudis SG; Ast MP; Sculco TP; Jules-Elysee KM
    J Bone Joint Surg Am; 2022 Feb; 104(3):265-270. PubMed ID: 34898530
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting postoperative transfusion in elective total HIP and knee arthroplasty: Comparison of different machine learning models of a case-control study.
    Huang Z; Martin J; Huang Q; Ma J; Pei F; Huang C
    Int J Surg; 2021 Dec; 96():106183. PubMed ID: 34863965
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
    Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
    Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.
    Zhu J; Xu C; Jiang Y; Zhu J; Tu M; Yan X; Shen Z; Lou Z
    Orthop Surg; 2024 Aug; 16(8):2066-2080. PubMed ID: 38951965
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.