Pain Management

Predicting the need for syrinx shunting after posterior fossa decompression in Chiari I malformation: a real-world machine learning analysis.

OBJECTIVE: The aim of this study was to develop and validate a machine learning (ML) algorithm to predict the delayed need for syrinx shunt placement following posterior fossa decompression (PFD) for Chiari malformation type I (CM-I) with concurrent syringomyelia. METHODS: This multicenter retrospective cohort study utilized the TriNetX network to identify patients undergoing index PFD for CM-I an...

Research on influencing factors of college students' dependence on AI painting tools: A hybrid method based on SEM-ANN.

With the recent advances in technology, AI painting tools have become a mainstream part of digital creation. However, limited research has examined students' dependence on AI-powered drawing tools. Therefore, this study uses college students as a case study to analyze the key factors influencing dependence on AI painting tools. Drawing on the I-PACE model, we constructed a dependence model for AI ...

Breaking the Autophagy-Oxidative Stress Vicious Cycle in Alzheimer's Disease: Lactiflorin Unlocks P62 Dual Phosphorylation via ULK1 Targeting.

ETHNOPHARMACOLOGICAL RELEVANCE: The dried root of Paeonia lactiflora Pall. has a long history of medicinal use in traditional Chinese medicine. Classi...

Advances in New Approach Methodologies at FDA's Division of Applied Regulatory Science.

New Approach Methodologies (NAMs) represent a paradigm shift in drug development and regulatory science, offering human-relevant alternatives to tradi...

Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.

We developed and deploy a real‑time, electronic health record‑integrated machine learning phenotype to identify emergency department patients with opi...

Predicting Craniospinal Surgery in Pediatric Achondroplasia: Benchmarking Statistical Inference and Explainable AI Under Rare-Disease Constraints.

PURPOSE: To develop a framework that benchmarks penalized statistical inference against explainable artificial intelligence (XAI) methods for predicti...

A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity.

Activity-dependent synaptic plasticity is a fundamental learning mechanism that shapes the connectivity and activity of neural circuits. Existing comp...

Sinogram-based regularization parameter learning for 2D penalized maximum-likelihood PET image reconstruction.

Positron Emission Tomography (PET) is a molecular imaging technique that creates an image of radiopharmaceutical distribution using acquired sinogram ...

Examining Increased Ritalin and Adderall Use Among Low-Income People Who Use Drugs: Mixed Methods Study.

BACKGROUND: Popular discourse often frames prescription stimulants Ritalin and Adderall as drugs for teens and emerging adults with greater financial ...

Unsupervised Identification of Population Clusters With Distinct Low Back Pain Profiles.

BACKGROUND CONTEXT: Low back pain (LBP) is a leading cause of disability worldwide, yet population-level stratification of LBP risk remains limited. U...

CT-Guided Procedures: Tips and Tricks-A Practical Review for Contemporary Musculoskeletal Intervention.

Computed tomography (CT)-guided musculoskeletal interventions (MSK-CTg-IR) are integral to modern interventional radiology, offering precise anatomic ...

Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.

BACKGROUND: Artificial intelligence (AI) is increasingly explored in veterinary neurology for pattern recognition, prediction and clinical decision su...

Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.

INTRODUCTION: Cancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical o...

Development of a Multimodal Machine Learning Model for Predicting Second Osteoporotic Vertebral Compression Fractures.

PURPOSE: Osteoporosis increases the probability of osteoporotic vertebral compression fractures (OVCF), and the resultant pain severely limits physica...

State-Specific Segmental Abnormalities of White Matter Structure and Function in Pediatric Bipolar Disorders.

OBJECTIVE: White matter (WM), as the critical infrastructure for neural communication, is implicated in pediatric bipolar disorder (PBD) pathophysiolo...

Integration of Artificial Intelligence Models and Logistic Regression to Increase the Diagnostic Accuracy of Acute Appendicitis.

BACKGROUND/AIM: In patients with acute abdominal pain (AAP), the accuracy of artificial intelligence (AI) models in the diagnosis of acute appendiciti...

Visual representation bias in artificial intelligence-generated depictions of anesthesia, pain, and intensive care professionals.

ObjectiveTo evaluate the demographic and hierarchical representation biases in artificial intelligence-generated depictions of healthcare professional...

Pain in laboratory fishes: What we know and what we need to know.

The scientific exploration of the capacity of fishes to experience pain has grown since the first identification of nociceptors in rainbow trout in 20...

Are effects of task-related stressors and academic self-management on college students' AI stickiness consistent across the DeepSeek transition?

Steeped in outcome-driven educational ecosystems, college students, especially the first-years facing the transition from high school to university wi...

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty.

Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial as...