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Skip search results from other journals and go to results- 1095 Journal of Medical Internet Research
- 438 JMIR Research Protocols
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(A) 3rd-month follow-up, (B) 6th-month follow-up, (C) 12th-month follow-up, and (D) 24th-month follow-up; nodes represent individual symptoms and edges to illustrate the correlations between symptoms for each interval. Each node’s color and size represent the degree of the symptom, with darker colors and larger size indicating a higher number of symptoms correlated with it.
JMIR Public Health Surveill 2025;11:e72221
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Finally, the high-need responder (n=17) was characterized by high engagement (82.4% response rate), moderate conformity (18.5% error rate), and notably, a high rate of inbound messages (3.1 per d) and requests for help (64.7% requesting a call outside of a check-in window).
J Med Internet Res 2025;27:e72875
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Relationship Between Fear of Missing Out and Social Media Fatigue: Cross-Lagged Panel Design
J Med Internet Res 2025;27:e75701
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Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study
The presence of any of the following criteria (ie, abbreviated with the letters B, D, M, and N) were used to qualify the hospitalizations for inclusion in the PWUD cohort:
B (Biomarkers): In line with a previous study, positive urine toxicology for drugs or medications for SUD (eg, cocaine, amphetamine, methadone, suboxone, fentanyl, opiate, oxycodone), positive HCV antibody with positive or quantifiable HCV viral load [29]
D (Diagnostic codes): Presence of ICD-9 and or ICD-10 code for overdose, substance use
JMIR AI 2025;4:e63147
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Wang et al [13] differentiated heat and cold patterns in rheumatoid arthritis through cluster and factor analyses to guide clinical medication. Wu et al [48] investigated TCM cold and hot constitutions using pulse wave parameters such as augmentation index and heart rate variability. Although some individual studies have applied artificial intelligence to differentiate cold and hot properties, most efforts have been limited to identifying the properties of Chinese herbal medicines.
JMIR Med Inform 2025;13:e64725
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Primary outcomes in the intention-to-treat populationa.
a Data are mean (SD).
b Cohen d values represent effect sizes for comparisons between each VR group and the non-VR group, or between the VR moderate- and high-intensity groups. η² represents the effect size from ANOVA for the comparison among all 3 groups.
c HAMD-17: Hamilton Depression Rating Scale.
d VR: virtual reality.
e Not applicable.
J Med Internet Res 2025;27:e72021
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