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Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

In the study by Alsentzer and Kim [38], they explored extractive summarization of discharge notes from the MIMIC-III database. They estimated an upper bound on extractive summarization by measuring how much of the information in a discharge summary can be found elsewhere in the patient’s EHR notes using concept unique identifiers. To support future summarization tasks, they also developed a long short-term memory model to label word-level topics in the “history of present illness” sections.

Mahshad Koohi Habibi Dehkordi, Yehoshua Perl, Fadi P Deek, Zhe He, Vipina K Keloth, Hao Liu, Gai Elhanan, Andrew J Einstein

JMIR Med Inform 2025;13:e66476

Efficacy and Safety of Electroacupuncture Through Nerve Stimulation in Patients With Anxiety Disorders: Protocol for a Randomized, Assessor-Blind, Three-Arm, Parallel-Group Clinical Trial

Efficacy and Safety of Electroacupuncture Through Nerve Stimulation in Patients With Anxiety Disorders: Protocol for a Randomized, Assessor-Blind, Three-Arm, Parallel-Group Clinical Trial

Results will be presented with means, 97.5% CIs, and P values. In addition, if significant baseline differences exist between groups, an Analysis of Covariance (ANCOVA), adjusted for baseline values as a covariate, will be conducted. The analysis of secondary outcome variables will adhere to the same methodology used for the primary outcomes.

In Chul Jung, Dong-Hoon Kang, Sunyoung Choi, Yujin Choi, Ojin Kwon, Hye Jeong Kook, Daeun Lee, Yang-Chun Park, Jieun Kim

JMIR Res Protoc 2025;14:e68166

Hand Motion Control Ability Between Young and Older Adults: Comparative Study

Hand Motion Control Ability Between Young and Older Adults: Comparative Study

Participants in trial 2 completed 1.77 more rotations compared to trial 1 (B=1.77, SE 0.59, z=3.00, P=.003), and participants in trial 3 completed 2.31 more rotations compared to trial 1 (B=2.31, SE 0.72, z=3.20, P=.001). These findings suggest a learning effect across trials, with participants demonstrating better performance in subsequent trials.

Jung-Soon Kim, Hui-Jun Kim, Minju Kim, Sung-Hee Kim

JMIR Form Res 2025;9:e65224

A Comprehensive Profiling System Integrating Myers-Briggs Type Indicator (MBTI) and Dominance, Influence, Steadiness, and Conscientiousness (DISC) for Personalized Health Training: Correlational Analysis and Usability Evaluation

A Comprehensive Profiling System Integrating Myers-Briggs Type Indicator (MBTI) and Dominance, Influence, Steadiness, and Conscientiousness (DISC) for Personalized Health Training: Correlational Analysis and Usability Evaluation

It categorizes personalities into 16 types based on cognitive preferences and information processing styles, using 4 dichotomies: Extraversion (E)–Introversion (I), Sensing (S)–Intuition (N), Thinking (T)–Feeling (F), and Judging (J)–Perceiving (P). MBTI is widely used to understand interpersonal relationships, decision-making styles, and job performance [10]. However, its focus on static personality classifications can limit its ability to reflect situational flexibility [9].

Donghyun Kim, Dong Hun Lee, Mi Kyung Hwang

JMIR Hum Factors 2025;12:e73397

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

Using a random-effects model, the meta-analysis demonstrated that the ML-based models (AUROC: 0.88, 95% CI 0.86‐0.90; I²=97.5%; P To statistically confirm whether this observed performance difference was significant, a meta-regression analysis was conducted with model type (ML-based vs CRS) as the moderator.

Min-Young Yu, Hae Young Yoo, Ga In Han, Eun-Jung Kim, Youn-Jung Son

J Med Internet Res 2025;27:e76215

Impact of Ecological Momentary Assessment Participation on Short-Term Smoking Cessation: quitSTART Ecological Momentary Assessment Incentivization Randomized Trial

Impact of Ecological Momentary Assessment Participation on Short-Term Smoking Cessation: quitSTART Ecological Momentary Assessment Incentivization Randomized Trial

Mean EMAs completed in the incentivized arm was 13.3 (SD 11.2, range 0‐40, average completion rate of 31.7% out of 42 total EMA prompts) and 4.7 (SD 5.8, range 0‐28, average completion rate of 11.2% out of 42 total EMA prompts) in the nonincentivized arm (P Smoking cessation outcomes overall and by group. a EMA: ecological momentary assessment.

Kara P Wiseman, Alex Budenz, Leeann Siegel, Yvonne M Prutzman

J Med Internet Res 2025;27:e67630