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Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study

Previous studies have demonstrated an independent association between FEV1 and the risk of AE-COPD in both young and old patients with COPD [6,7]. FEV1 is a key predictor of AE-COPD in the majority of existing prediction models [8]. However, its association with exacerbations is relatively weak, limiting its role as a predictive factor [9].

Eun-Tae Jeon, Heemoon Park, Jung-Kyu Lee, Eun Young Heo, Chang Hoon Lee, Deog Kyeom Kim, Dong Hyun Kim, Hyun Woo Lee

J Med Internet Res 2025;27:e69785

Combining Artificial Intelligence and Human Support in Mental Health: Digital Intervention With Comparable Effectiveness to Human-Delivered Care

Combining Artificial Intelligence and Human Support in Mental Health: Digital Intervention With Comparable Effectiveness to Human-Delivered Care

Qualitative feedback and the UES were collected via Qualtrics (SAP SE). The safety end points were manually logged by research coordinators and clinicians following participant contact where events were reported (eg, phone calls, clinical reviews, and emails). Sample characteristics of the digital program group for both ITTa and PPb samples. a ITT: intention to treat. b PP: per protocol. c GAD-7: 7-item Generalized Anxiety Disorder Scale. d PHQ-9: 9-item Patient Health Questionnaire.

Clare E Palmer, Emily Marshall, Edward Millgate, Graham Warren, Michael Ewbank, Elisa Cooper, Samantha Lawes, Alastair Smith, Chris Hutchins-Joss, Jessica Young, Malika Bouazzaoui, Morad Margoum, Sandra Healey, Louise Marshall, Shaun Mehew, Ronan Cummins, Valentin Tablan, Ana Catarino, Andrew E Welchman, Andrew D Blackwell

J Med Internet Res 2025;27:e69351

Clinical Efficacy and Safety of the Herbal Prescription, HH333, in Preventing Recurrent Stroke in Patients With Ischemic Stroke Induced by Small-Vessel Disease: Protocol for Multicenter, Double-Blind, Randomized, Prospective, Pilot Clinical Trial

Clinical Efficacy and Safety of the Herbal Prescription, HH333, in Preventing Recurrent Stroke in Patients With Ischemic Stroke Induced by Small-Vessel Disease: Protocol for Multicenter, Double-Blind, Randomized, Prospective, Pilot Clinical Trial

Jung et al [31] developed an equation model that predicts the probability of each pattern identification diagnosis given variables for stroke pattern identification and is being used for Korean medicine's standardized diagnosis of patients with stroke. Using a stroke pattern identification prediction model, the change in each pattern identified after HH333 administration was collected to obtain data for stroke treatment in Korean medicine.

Han-Gyul Lee, Seungwon Kwon, Woo-Sang Jung, Sang-Kwan Moon, Cheol-Hyun Kim, Dong-Jun Choi

JMIR Res Protoc 2025;14:e70953

Investigating Social Network Peer Effects on HIV Care Engagement Using a Fuzzy-Like Matching Approach: Cross-Sectional Secondary Analysis of the N2 Cohort Study

Investigating Social Network Peer Effects on HIV Care Engagement Using a Fuzzy-Like Matching Approach: Cross-Sectional Secondary Analysis of the N2 Cohort Study

For the linear network autocorrelation models adjusted for HIV status (Table 2, models f-j), HIV status had a significant negative association with care engagement for all network models: confidant and sexual (β=−0.268, SE 0.048; P Results of the unadjusted and adjusted network autocorrelation assessing the peer effects of status-neutral HIV outcomes among 412 Chicago-based Black sexually minoritized and gender expansive people in the Neighborhoods and Networks (N2) cohort study (2018‐2019), by network type.

Cho-Hee Shrader, Dustin T Duncan, Redd Driver, Juan G Arroyo-Flores, Makella S Coudray, Raymond Moody, Yen-Tyng Chen, Britt Skaathun, Lindsay Young, Natascha del Vecchio, Kayo Fujimoto, Justin R Knox, Mariano Kanamori, John A Schneider

JMIR Public Health Surveill 2025;11:e64497

Podcasts in Mental, Physical, or Combined Health Interventions for Adults: Scoping Review

Podcasts in Mental, Physical, or Combined Health Interventions for Adults: Scoping Review

The age distribution of participants spanned from young adulthood (aged ≥18 y) to older adults (aged up to 90 y), with the mean age of participants most commonly between their late 20s to mid-50s. Methodologically, the included studies primarily used randomized controlled trial designs. The assessment periods varied substantially, ranging from immediate posttest intervention evaluations to longitudinal follow-ups (12 months after baseline).

Elizabeth M Dascombe, Philip J Morgan, Ryan J Drew, Casey P Regan, Gabrielle M Turner-McGrievy, Myles D Young

J Med Internet Res 2025;27:e63360