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Skip search results from other journals and go to results- 1157 Journal of Medical Internet Research
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(C) Evaluation of the emotional state during craving halfway through the digital monitoring period (third month), and at the end of it. (D) Data preprocessing is required for all 3 data sources. (E) Training in a machine learning predictive model in neural networks. (F) creation of a graphic user interface for clinical use.
The minimum sample size was determined using Epidat 4.2 software (Xunta de Galicia), following the equation for estimating the comparison between independent means.
JMIR Res Protoc 2025;14:e71374
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The State of Remote Patient Monitoring for Chronic Disease Management in the United States
J Med Internet Res 2025;27:e70422
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development (R)
Acquisition of external graphics designer (G)
Selection of software and metrics (J)
Learning to work with existing technologies for scheduling and publishing (AF)
Sudden unexpected team member change (U)
Outputs
Extracting key teaching points from top PECARN articles into tweet (X)
Tweet frequency established at the same time each week (3 times per week) (AH)
Tweet visual structure: Intentional or strategic polls, emojis, bullet points, and multimedia such as images and videos (AA, AB, AC, C,
JMIR Form Res 2025;9:e59481
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Preventing Urgent Pediatric Readmissions: The Need for and Promise of Real-Time Monitoring
JMIR Pediatr Parent 2025;8:e60802
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The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study
We first evaluated how Ro CA evaluated patient drawings (Figure 3 A-C). Ro CA classified 97% (44/46) of the cubes correctly, 91% (42/46) of the infinities correctly, and 98% (45/46) of the clocks correctly. We next calculated the accuracy of Ro CA for each drawing individually (Figure 3 D). We compared the accuracy of each drawing to its statistical baseline by bootstrapping, resampling the accuracy, and counting the number of times it fell below the random classifier.
J Med Internet Res 2025;27:e66735
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