Medicare

Electroencephalography microstates predict 12-h abstinence-induced craving changes in young smokers.

Nicotine abstinence inhibits the function of the mesolimbic dopamine system to enhance craving. EEG microstates may provide spatiotemporal characteristics of global brain neuronal activity. However, little is known about the temporal dynamics and spatial topography of microstates in young smokers after abstinence. At the same time, in order to explore the neurophysiological mechanisms of craving i...

The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 2. COMI item-content, scoring, language versions, and applications.

In 1998, an international and multidisciplinary group of experts (from the fields of spine surgery, physiotherapy, occupational therapy, rheumatology, primary care medicine, internal medicine, health economics and epidemiology) proposed a short multidimensional series of core outcome items for use in patients with low back disorders. In 2005 and 2006, two independent research groups published stud...

The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 1. Development of the COMI, interpretation of scores, and use in research.

In 1998, an international and multidisciplinary group of experts (from the fields of spine surgery, physiotherapy, occupational therapy, rheumatology,...

Step up to triple therapy versus switch to dual bronchodilator therapy in patients with COPD on an inhaled corticosteroid/long-acting β-agonist: post-hoc analyses of KRONOS.

BACKGROUND: In people with chronic obstructive pulmonary disease (COPD) on inhaled corticosteroid/long-acting β-agonist (ICS/LABA) therapy, the Global...

Enhancing biomarker based oncology trial matching using large language models.

Clinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patient's eligib...

Improving Foundation Model for Endoscopy Video Analysis via Representation Learning on Long Sequences.

Recent advancements in endoscopy video analysis have relied on the utilization of relatively short video clips extracted from longer videos or million...

Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic ...

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.

The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since...

Advancing emergency upper extremity care: A pilot study of ChatGPT's potential role in diagnosing and managing hand and wrist trauma.

PURPOSE: Hand and wrist trauma is a frequent cause of emergency room (ER) visits. However, hospitals often lack immediate hand specialist coverage. Th...

Deep Learning-Based Prediction of Decoy Spectra for False Discovery Rate Estimation in Spectral Library Searching.

With the advantage of extensive coverage, predicted spectral libraries are becoming an attractive alternative in proteomic data analysis. As a popular...

Assessing the Usability of ChatGPT Responses Compared to Other Online Information in Hand Surgery.

BACKGROUND: ChatGPT is a natural language processing tool with potential to increase accessibility of health information. This study aimed to: (1) ass...

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST...

Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated...

An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.

PURPOSE: Volumetric-modulated arc therapy (VMAT) treatment planning allows a compromise between a sufficient coverage of the planning target volume (P...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tac...

Opportunities and Challenges for Large Language Models in Primary Health Care.

Primary Health Care (PHC) is the cornerstone of the global health care system and the primary objective for achieving universal health coverage. China...

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in...

Student dropout prediction through machine learning optimization: insights from moodle log data.

Student attrition and academic failure remain pervasive challenges in education, often occurring at substantial rates and posing considerable difficul...

SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly...

Enhancement of long-horizon task planning via active and passive modification in large language models.

This study proposes a method for generating complex and long-horizon off-line task plans using large language models (LLMs). Although several studies ...