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Influences Of Athletic Trainers' Return-To-Activity Assessments For Patients With An Ankle Sprain, Ryan S. Mccann, Cailee E. Welch Bacon, Ashley M. B. Suttmiller, Phillip A. Gribble, Julie M. Cavallario
Influences Of Athletic Trainers' Return-To-Activity Assessments For Patients With An Ankle Sprain, Ryan S. Mccann, Cailee E. Welch Bacon, Ashley M. B. Suttmiller, Phillip A. Gribble, Julie M. Cavallario
Rehabilitation Sciences Faculty Publications
Context: Athletic trainers (ATs) inconsistently apply rehabilitation-oriented assessments (ROASTs) when deciding return-to-activity readiness for patients with an ankle sprain. Facilitators and barriers that are most influential to ATs' assessment selection remain unknown.
Objective: To examine facilitators of and barriers to ATs' selection of outcome assessments when determining return-to-activity readiness for patients with an ankle sprain.
Design: Cross-sectional study.
Setting: Online survey.
Patients or other participants: We sent an online survey to 10 000 clinically practicing ATs. The survey was accessed by 676 individuals, of whom 574 submitted responses (85% completion rate), and 541 respondents met the inclusion criteria.
Main outcome …
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Community & Environmental Health Faculty Publications
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …