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Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation

Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation

Profile accuracy: H=high, M=medium, L=low. AUC: area under the curve; M: mixed; N: negative; P: positive; TPR: true-positive rate; TNR: true-negative rate. The above summary (Figure 2) presents results for all pilot study patients to show performance and overall results. However, the individual prognostic patient profile as used in IMPT clinical assessment provides clearly presented summary results for each patient.

Fredrick Zmudzki, Rob J E M Smeets, Jan S Groenewegen, Erik van der Graaff

JMIR Rehabil Assist Technol 2025;12:e65890

Improving Recruitment Through Social Media and Web-Based Advertising to Evaluate the Genetic Risk and Long-Term Complications in Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis: Community-Based Survey

Improving Recruitment Through Social Media and Web-Based Advertising to Evaluate the Genetic Risk and Long-Term Complications in Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis: Community-Based Survey

H White: Hispanic White; NH White: Non-Hispanic White. Completed interest surveys and enrollment rate were broken down by recruitment channel. The SJS Foundation website produced 149 responses/69 enrolled (46.3% enrollment rate). Google Ads followed with 201 responses/56 enrolled. VUMC Facebook ads contributed 163 responses/25 enrolled (15.3% enrollment rate). Instagram had 7 responses/1 enrolled (14.3% enrollment rate).

Elizabeth A Williams, Michelle D Martin-Pozo, Alexis H Yu, Krystyna Daniels, Madeline Marks, April O'Connor, Elizabeth J Phillips

J Med Internet Res 2025;27:e63712