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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

The PMIS (Pearson r=−0.76; P As we examined the sensitivity and specificity data to choose cut scores, we chose to favor sensitivity to minimize missing individuals with true disease in this sample of patients considered high risk because of their cognitive concerns. The cut scores for a positive result on the 5-Cog components were as follows: PMIS ≤6 (range 0-8), Symbol Match ≤25 (range 0-65), and s MCR >5 (range 0-7).

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

Conversion of Sensitive Data to the Observational Medical Outcomes Partnership Common Data Model: Protocol for the Development and Use of Carrot

Conversion of Sensitive Data to the Observational Medical Outcomes Partnership Common Data Model: Protocol for the Development and Use of Carrot

For example, assume a field “Sex” is provided with the value “M,” and this has previously been manually mapped to the OMOP concept 8507 “Male” in one scan R=report. In the case where a later scan report is uploaded with another field “Gender” and the value “M,” the system automatically applies the previous mapping rule to this value in this field in the new scan report.

Samuel Cox, Erum Masood, Vasiliki Panagi, Calum Macdonald, Gordon Milligan, Scott Horban, Roberto Santos, Chris Hall, Daniel Lea, Simon Tarr, Shahzad Mumtaz, Emeka Akashili, Andy Rae, Esmond Urwin, Christian Cole, Aziz Sheikh, Emily Jefferson, Philip Roy Quinlan

JMIR Res Protoc 2025;14:e60917

Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study

The application of this mapping to the data was performed using R version 4.3.2 (R Foundation for Statistical Computing). The full list of diagnosis names corresponding to ADRD diagnosis categories is provided in Multimedia Appendix 1. To assess associations between clusters and sex, as well as ADRD diagnoses, we used the chi-square test.

Matthew West, You Cheng, Yingnan He, Yu Leng, Colin Magdamo, Bradley T Hyman, John R Dickson, Alberto Serrano-Pozo, Deborah Blacker, Sudeshna Das

JMIR Aging 2025;8:e65178