Results 201 to 210 of about 19,841,559 (252)

Determinants of Body Mass Index in Early Systemic Sclerosis: Implications for Nutritional Risk Stratification

open access: yesArthritis Care &Research, EarlyView.
Objective Gastrointestinal (GI) involvement can lead to malnutrition in patients with systemic sclerosis (SSc). Body mass index (BMI) remains the most widely used marker to screen nutritional status. We aimed to identify predictors of lower BMI in patients with SSc. Methods Patients with SSc from a prospective US cohort meeting 2013 American College of
Ali Y. Ayla   +8 more
wiley   +1 more source

One-core neuron deep learning for time series prediction. [PDF]

open access: yesNatl Sci Rev
Peng H   +5 more
europepmc   +1 more source

Generative AI in clinical practice: how should rheumatologists evaluate and monitor emerging tools?

open access: yesArthritis Care &Research, Accepted Article.
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo   +2 more
wiley   +1 more source

The content and reporting completeness of physical therapist‐led interventions for glenohumeral osteoarthritis: a systematic review

open access: yesArthritis Care &Research, Accepted Article.
Objectives This study synthesised the content and reporting completeness of published physical therapist‐led interventions for glenohumeral osteoarthritis (GHOA). Methods This systematic review undertook electronic database searches in PubMed, EMBASE, Cochrane, CINAHL and PEDro (until June 2025) for any peer‐reviewed articles/studies describing ...
Daha G Muhammad   +5 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +3 more
wiley   +1 more source

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