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Prenatal opioid exposure, neonatal abstinence syndrome diagnosis, and child welfare involvement.
Child Abuse Negl
January 2025
Social Science Research Institute, Pennsylvania State University, United States of America. Electronic address:
Background: Neonatal abstinence syndrome (NAS), or withdrawal from prenatal opioid exposure at birth, can trigger a referral to child protective services (CPS). However, there is some evidence of selection into NAS diagnosis because NAS screening is not universal. Such referrals may protect the infant, help connect the mother to services, or cause harm.
View Article and Find Full Text PDFAdaptive Cost-Benefit Control Fueled by Striatal Dopamine.
Annu Rev Neurosci
January 2025
Department of Cognitive and Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, Rhode Island, USA; email:
The twenty-first century has brought forth a deluge of theories and data shedding light on the neural mechanisms of motivated behavior. Much of this progress has focused on dopaminergic dynamics, including their signaling properties (how do they vary with expectations and outcomes?) and their downstream impacts in target regions (how do they affect learning and behavior?). In parallel, the basal ganglia have been elevated from their original implication in motoric function to a canonical circuit facilitating the initiation, invigoration, and selection of actions across levels of abstraction, from motor to cognitive operations.
View Article and Find Full Text PDFEvaluation and Comparison of the Academic Quality of Open-Access Mega Journals and Authoritative Journals: Disruptive Innovation Evaluation.
J Med Internet Res
January 2025
Henan Research Center for Science Journals, Xinxiang Medical University, Xinxiang, China.
Background: Some scholars who are skeptical about open-access mega journals (OAMJs) have argued that low-quality papers are often difficult to publish in more prestigious and authoritative journals, and OAMJs may be their main destination.
Objective: This study aims to evaluate the academic quality of OAMJs and highlight their important role in clinical medicine. To achieve this aim, authoritative journals and representative OAMJs in this field were selected as research objects.
Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study.
J Med Internet Res
January 2025
Vibrent Health, Inc, Fairfax, VA, United States.
Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used.
Objective: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health's (NIH) All of Us Research Program (AOU).
A Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach.
J Bone Joint Surg Am
November 2024
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed.
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