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Improving Early Dementia Detection Among Diverse Older Adults With Cognitive Concerns With the 5-Cog Paradigm: Protocol for a Hybrid Effectiveness-Implementation Clinical Trial

Improving Early Dementia Detection Among Diverse Older Adults With Cognitive Concerns With the 5-Cog Paradigm: Protocol for a Hybrid Effectiveness-Implementation Clinical Trial

(D) IUH: negative 5-Cog result. At both sites, a paper token (Figure 4) is used as an additional feature to help ensure that care providers review the patients’ 5-Cog results. Patients are handed this token after they complete the 5-Cog battery and are asked to hand it to their care provider at their scheduled visit, generally within 30 minutes after the 5-Cog battery administration. Token to alert care provider for patient's 5-Cog participation.

Rachel Beth Rosansky Chalmer, Emmeline Ayers, Erica F Weiss, Nicole R Fowler, Andrew Telzak, Diana Summanwar, Jessica Zwerling, Cuiling Wang, Huiping Xu, Richard J Holden, Kevin Fiori, Dustin D French, Celeste Nsubayi, Asif Ansari, Paul Dexter, Anna Higbie, Pratibha Yadav, James M Walker, Harrshavasan Congivaram, Dristi Adhikari, Mairim Melecio-Vazquez, Malaz Boustani, Joe Verghese

JMIR Res Protoc 2025;14:e60471

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach

The predictor period was defined as the first 30 days of MMP app use, and the outcome period was set between 30 days and 6 months (183 d) from the first app use (Figure 2). There were 680 users who recorded a response in the predictor period, 182 users who recorded a response in the outcome period, and 180 users who had a response in both the predictor and outcome periods. Therefore, 180 users were selected for this study. Overview of study timelines and machine learning model approach.

James Skoric, Anna M Lomanowska, Tahir Janmohamed, Heather Lumsden-Ruegg, Joel Katz, Hance Clarke, Quazi Abidur Rahman

JMIR Med Inform 2025;13:e67178

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Each observation was represented by the triplet {X,T,S}, where X⊆Rd is a d-dimensional feature vector, T∈(0,Emax] is an observed event or censoring time over a finite time horizon, and S∈{0,1} indicates whether T is a right-censoring time (S=0) or an event time (S=1). The observed time T is the minimum of the event time E and the right-censoring time C, that is, T=min(E, C).

De Rong Loh, Elliot D Hill, Nan Liu, Geraldine Dawson, Matthew M Engelhard

JMIR AI 2025;4:e62985