Publications by authors named "S B Overman"
Article Synopsis
- This study investigates the fairness of machine learning-based clinical decision support tools (CDSTs) used in predicting outcomes for patients undergoing shoulder surgeries based on demographic factors like ethnicity, sex, and age.
- Although the accuracy of predictions varied slightly across these demographic groups, results indicated that the biases in predictions were minimal, with no significant unfairness observed in predicting minimal clinically important differences (MCID) and only a tiny percentage demonstrating bias in predicting substantial clinical benefits.
- The findings suggest that the CDSTs used for shoulder arthroplasties are generally fair, posing little risk of bias that could lead to unequal treatment for different patient demographics.
Background: Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative time points.
Methods: Clinical data from 2270 aTSA and 4198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative time points.
Background: We propose a new clinical assessment tool constructed using machine learning, called the Shoulder Arthroplasty Smart (SAS) score to quantify outcomes following total shoulder arthroplasty (TSA).
Methods: Clinical data from 3667 TSA patients with 8104 postoperative follow-up reports were used to quantify the psychometric properties of validity, responsiveness, and clinical interpretability for the proposed SAS score and each of the Simple Shoulder Test (SST), Constant, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES), University of California Los Angeles (UCLA), and Shoulder Pain and Disability Index (SPADI) scores.
Results: Convergent construct validity was demonstrated, with all 6 outcome measures being moderately to highly correlated preoperatively and highly correlated postoperatively when quantifying TSA outcomes.
Background: A machine learning analysis was conducted on 5774 shoulder arthroplasty patients to create predictive models for multiple clinical outcome measures after anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). The goal of this study was to compare the accuracy associated with a full-feature set predictive model (ie, full model, comprising 291 parameters) and a minimal-feature set model (ie, abbreviated model, comprising 19 input parameters) to predict clinical outcomes to assess the efficacy of using a minimal feature set of inputs as a shoulder arthroplasty clinical decision-support tool.
Methods: Clinical data from 2153 primary aTSA patients and 3621 primary rTSA patients were analyzed using the XGBoost machine learning technique to create and test predictive models for multiple outcome measures at different postoperative time points via the full and abbreviated models.