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Analyzing Trends in Suicidal Thoughts Among Patients With Psychosis in India: Exploratory Secondary Analysis of Smartphone Ecological Momentary Assessment Data

Analyzing Trends in Suicidal Thoughts Among Patients With Psychosis in India: Exploratory Secondary Analysis of Smartphone Ecological Momentary Assessment Data

The results in this table do not account for missing values or days with no survey forms received or no ninth item filled in the mood survey. a Spearman correlation between Patient Health Questionnaire-9 total and SI score at ecological momentary assessment prompts was 0.47 (n=3176, P=10 and suicidal ideation score ≥1 was 0.32 (n=245, P b Disregarding the episodic pattern. c To understand the difference between “days between successive SI instances” and “episode duration” (refer to the episode data in Table 3),

Ameya P Bondre, Aashish Ranjan, Ritu Shrivastava, Deepak Tugnawat, Nirmal Kumar Chaturvedi, Anant Bhan, Snehil Gupta, Abhijit R Rozatkar, Srilakshmi Nagendra, Siddharth Dutt, Soumya Choudhary, Preethi V Reddy, Urvakhsh Meherwan Mehta, John A Naslund, John Torous

JMIR Form Res 2025;9:e67745

The Effectiveness of Social Media Campaigns in Improving Knowledge and Attitudes Toward Mental Health and Help-Seeking in High-Income Countries: Scoping Review

The Effectiveness of Social Media Campaigns in Improving Knowledge and Attitudes Toward Mental Health and Help-Seeking in High-Income Countries: Scoping Review

We only reported differences that were statistically tested and for which P values, CIs, or effect sizes were available. Descriptions and examples of social media–related outcomes to be extracted from studies. a Exposure, reach, and low-, medium-, and high-level engagement were measured using a version of key performance indicators and metrics related to social media use in health promotion adapted from Neiger et al [26].

Ruth Plackett, Jessica-Mae Steward, Angelos P Kassianos, Marvin Duenger, Patricia Schartau, Jessica Sheringham, Silvie Cooper, Lucy Biddle, Judi Kidger, Kate Walters

J Med Internet Res 2025;27:e68124

Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study

Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study

Dropout layer (P=.50): randomly inactivate 50% of neurons to prevent overfitting. Linear layer (128×128): fully connected, keeping the same dimension. The second CNN module Convolutional layer (128×64): performs the convolution operation again. Maximum pooling layer (kernel_size=2, stride=2): reduces the dimension again to improve the generalization ability of the model. The second phase of MLP Flatten layer: flatten into a vector for feeding into the MLP layer.

Hui-Chiao Yang, Angelica Te-Hui Hao, Shih-Chia Liu, Yu-Cheng Chang, Yao-Te Tsai, Shao-Jen Weng, Ming-Cheng Chan, Chen-Yu Wang, Yeong-Yuh Xu

JMIR Med Inform 2025;13:e64592

Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial

Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial

We will report P values up to 3 decimal places and report P values less than .001 as We expect a small amount of missing data due to noncompliance and loss to follow-up. As noted above, participants who withdraw will be asked for a reason for withdrawal to determine whether the missing information is random. Participants withdrawing prior to the procedure will be asked whether they intend to proceed to ICA.

Jeremy Petch, Juan Pablo Tabja Bortesi, Tej Sheth, Madhu Natarajan, Natalia Pinilla-Echeverri, Shuang Di, Shrikant I Bangdiwala, Karen Mosleh, Omar Ibrahim, Kevin R Bainey, Julian Dobranowski, Maria P Becerra, Katie Sonier, Jon-David Schwalm

JMIR Res Protoc 2025;14:e71726