Literature DB >> 34381635

Factors Associated with a Recommendation for Operative Treatment for Fracture of the Distal Radius.

David W G Langerhuizen1, Stein J Janssen2, Joost T P Kortlever3, David Ring3, Gino M M J Kerkhoffs2, Ruurd L Jaarsma1, Job N Doornberg1.   

Abstract

Background  Evidence suggests that there is substantial and unexplained surgeon-to-surgeon variation in recommendation of operative treatment for fractures of the distal radius. We studied (1) what factors are associated with recommendation for operative treatment of a fracture of the distal radius and (2) which factors are rated as the most influential on recommendation of operative treatment. Methods  One-hundred thirty-one upper extremity and fracture surgeons evaluated 20 fictitious patient scenarios with randomly assigned factors (e.g., personal, clinical, and radiologic factors) for patients with a fracture of the distal radius. They addressed the following questions: (1) Do you recommend operative treatment for this patient (yes/no)? We determined the influence of each factor on this recommendation using random forest algorithms. Also, participants rated the influence of each factor-excluding age and sex- on a scale from 0 (not at all important) to 10 (extremely important). Results  Random forest algorithms determined that age and angulation were having the most influence on recommendation for operative treatment of a fracture of the distal radius. Angulation on the lateral radiograph and presence or absence of lunate subluxation were rated as having the greatest influence and smoking status and stress levels the lowest influence on advice to patients. Conclusions  The observation that-other than age-personal factors have limited influence on surgeon recommendations for surgery may reflect how surgeon cognitive biases, personal preferences, different perspectives, and incentives may contribute to variations in care. Future research can determine whether decision aids-those that use patient-specific probabilities based on predictive analytics in particular-might help match patient treatment choices to what matters most to them, in part by helping to neutralize the influence of common misconceptions as well as surgeon bias and incentives. Level of Evidence  There is no level of evidence for the study. Thieme. All rights reserved.

Entities:  

Keywords:  artificial intelligence; decision making; deep learning; distal radial fracture; machine learning; predictive modelling; surgery

Year:  2021        PMID: 34381635      PMCID: PMC8328550          DOI: 10.1055/s-0041-1725962

Source DB:  PubMed          Journal:  J Wrist Surg        ISSN: 2163-3916


  23 in total

1.  The American Academy of Orthopaedic Surgeons Appropriate Use Criteria on the treatment of distal radius fractures.

Authors:  William C Watters; James O Sanders; Jayson Murray; Nilay Patel
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2.  Determinants of pain in patients with carpal tunnel syndrome.

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3.  Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT.

Authors:  Wendy E Bruinsma; Thierry G Guitton; Jon J P Warner; David Ring
Journal:  J Bone Joint Surg Am       Date:  2013-09-04       Impact factor: 5.284

4.  How many variables can humans process?

Authors:  Graeme S Halford; Rosemary Baker; Julie E McCredden; John D Bain
Journal:  Psychol Sci       Date:  2005-01

5.  Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

Authors:  Aditya V Karhade; Quirina C B S Thio; Paul T Ogink; Akash A Shah; Christopher M Bono; Kevin S Oh; Phil J Saylor; Andrew J Schoenfeld; John H Shin; Mitchel B Harris; Joseph H Schwab
Journal:  Neurosurgery       Date:  2019-07-01       Impact factor: 4.654

6.  Do Surgeons Treat Their Patients Like They Would Treat Themselves?

Authors:  Stein J Janssen; Teun Teunis; Thierry G Guitton; David Ring
Journal:  Clin Orthop Relat Res       Date:  2015-11       Impact factor: 4.176

7.  Early psychological stress after forearm nerve injuries: a predictor for long-term functional outcome and return to productivity.

Authors:  Jean-Bart Jaquet; Sandra Kalmijn; Paul D L Kuypers; Albert Hofman; Jan Passchier; Steven E R Hovius
Journal:  Ann Plast Surg       Date:  2002-07       Impact factor: 1.539

8.  Greater Tuberosity Fractures: Does Fracture Assessment and Treatment Recommendation Vary Based on Imaging Modality?

Authors:  Stein J Janssen; Hugo H Hermanussen; Thierry G Guitton; Michel P J van den Bekerom; Derek F P van Deurzen; David Ring
Journal:  Clin Orthop Relat Res       Date:  2016-01-21       Impact factor: 4.176

9.  Do Orthopaedic Surgeons Acknowledge Uncertainty?

Authors:  Teun Teunis; Stein Janssen; Thierry G Guitton; David Ring; Robert Parisien
Journal:  Clin Orthop Relat Res       Date:  2015-11-09       Impact factor: 4.176

10.  Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

Authors:  Adler Perotte; Rajesh Ranganath; Jamie S Hirsch; David Blei; Noémie Elhadad
Journal:  J Am Med Inform Assoc       Date:  2015-04-20       Impact factor: 4.497

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