Back Pain

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...

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 traditional preclinical models. This mini-review highlights recent advances in NAM development, validation, and regulatory application from FDA's Division of Applied Regulatory Science (DARS). We describe in silico NAMs including quantitative systems phar...

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...

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 ...

Proof of Concept of Large Language Models for Opioid Treatment Policy Surveillance: 97% Agreement With Subject Matter Experts.

OBJECTIVE: Policy surveillance typically involves detailed, time-consuming manual screening of policies for inclusion in a final dataset. This screeni...

Antimony- and NSAID-Induced Hepatotoxicity at the Molecular Crossroads: Redox, Inflammasome and Programmed Cell-Death Networks as Therapeutic Targets for Natural Hepatoprotective Agents.

Drug- and metal-induced liver damage (DILI/MILI) continues to be a predominant cause of acute hepatic failure globally, with two clinically significan...

Identifying the presence of disc herniations in lumbar spine MRI using Gemini 3.1 Pro.

PURPOSE: Lumbar disc herniation is associated with substantial morbidity, including low back pain, radicular leg pain (sciatica), sensory disturbance,...

What predicts postoperative opioid prescribing and consumption? A statewide analysis of surgical quality registry data.

OBJECTIVE: Tailoring postoperative opioid recommendations to patient needs requires nuanced understanding of factors contributing to post-discharge op...

Common Challenges in Predicting Opioid-related Outcomes Using Machine Learning.

Machine learning (ML) models have been commonly utilized to predict various opioid-related outcomes and risks, including post-operative opioid use, op...

Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.

Alcohol Use Disorder (AUD) is a prevalent neuropsychiatric condition affecting about 28 million adults in the USA, with few objective biomarkers to as...

Temporal Drivers of Opioid-Related ED Visits: An Ensemble Machine Learning Study With Consensus-Based Feature Attribution.

Identification of modifiable risk factors for prescription opioid use disorder (OUD)-related emergency department (ED) visits (ICD-10 F11.xx) is a cli...

Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the All of Us research program.

Background: Identifying patients at risk of opioid overdose in healthcare settings is critical, yet evidence on predictive models and their performanc...

Machine Learning Model for Predicting Suicide Risks Among Patients With Posttraumatic Stress Disorder Who Received Opioids.

BACKGROUND: The cooccurrence of posttraumatic stress disorder (PTSD) and opioid use heightens suicide risk. We aimed to develop and validate a machine...

Natural language processing tool for extracting information about opioid overdoses in the USA from case narratives in the violent death reporting system.

BACKGROUND: Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We ...

A subtype of ultrasonic vocalizations during palatable food consumption in rats identified by machine learning-assisted classification.

Identifying behavioral and physiological responses to rewarding stimuli is essential for understanding positive emotional states in animals and for in...

Predictive Models for Time to First Opioid Use Disorder or Opioid Overdose Among Older Adults.

BACKGROUND: Limited data exist on predictive models incorporating patient-reported and claims-based measures to identify older adults at risk for opio...

Activation of sigma non-opioid intracellular receptor 1 (S1R) receptor inhibits plexin domain containing 2 (Plxdc2) driven microglial inflammation to ameliorate depressive-like behaviours in mice.

BACKGROUND AND PURPOSE: Microglia are central regulators of neuroinflammation in depression. Drivers involved remain incompletely understood. Sigma no...

Implications for Access to Chiropractic Services in the Workers' Compensation System: A State-by-State Coverage Policy Comparison.

BACKGROUND: Chiropractic services are well-aligned with the goals of workers' compensation (WC) coverage in returning patients to work timely, safely,...

Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts.

BACKGROUND: Fentanyl overdose deaths are still increasing across the U.S. Even though the crisis is growing, we still do not fully understand which co...

From single targets to circuits diversified discovery and development strategies towards new paradigms for non-opioid analgesics.

The long-term use of opioids for analgesia is associated with serious adverse effects such as addiction and respiratory depression, as well as a poten...