This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (596) E-Health / Health Services Research and New Models of Care (586) Surgery, Trauma and Surgical Sciences (160) Postoperative Monitoring and Telemonitoring (150) Quality Improvement (270)

Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48801, first published .
Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study

Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study

Using Presurgical Biopsychosocial Features to Develop an Advanced Clinical Decision-Making Support Tool for Predicting Recovery Trajectories in Patients Undergoing Total Knee Arthroplasty: Protocol for a Prospective Observational Study

Authors of this article:

Karen Ribbons1, 2, 3 Author Orcid Image ;   Sarah Johnson1, 2, 4 Author Orcid Image ;   Elizabeth Ditton1, 2, 3 Author Orcid Image ;   Adrian Wills1, 2, 4 Author Orcid Image ;   Gillian Mason2 Author Orcid Image ;   Traci Flynn5 Author Orcid Image ;   Jodie Cochrane1, 2, 4 Author Orcid Image ;   Michael Pollack1, 3, 6 Author Orcid Image ;   Frederick Rohan Walker1, 2, 3 Author Orcid Image ;   Michael Nilsson1, 2, 3, 7 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Dowell K, Dluzniewski A, Casanova M, Allred C, Cady A, Baker R. International Knee Documentation Committee Subjective Knee Form Latent Growth Model Analysis: Assessing Recovery Trajectories. Healthcare 2024;12(10):1021 View
  2. Qin M, Dai X, Yang C, Su W. Development and Validation of a Nomogram for Evaluating the Incident Risk of Pain Catastrophizing Among Patients Who Have Severe Knee Osteoarthritis Awaiting Primary Total Knee Arthroplasty. The Journal of Arthroplasty 2025;40(3):602 View
  3. Ribbons K, Payne K, Ditton E, Johnson S, Wills A, Walker F, Pollack M, Nilsson M, Akkawi I. Determining patient activity goals and their fulfillment following total knee arthroplasty: Findings from the prospective, observational SuPeR Knee study. PLOS ONE 2025;20(1):e0317205 View
  4. Ribbons K, Cochrane J, Johnson S, Wills A, Ditton E, Dewar D, Broadhead M, Chan I, Dixon M, Dunkley C, Harbury R, Jovanovic A, Leong A, Summersell P, Todhunter C, Verheul R, Pollack M, Walker R, Nilsson M. Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty. Scientific Reports 2025;15(1) View
  5. Chen X, Yu F. The Impact of Attribution Training on Emotional and Functional Recovery in Elderly Patients Undergoing Total Knee Arthroplasty: A Single-Center Randomized Controlled Trial. Geriatric Orthopaedic Surgery & Rehabilitation 2025;16 View