Results 191 to 200 of about 8,481,699 (293)

Cracking the Code: Which Ocular Symptoms Predict Dry Eye Signs? Insights From a Large International Sicca Registry

open access: yesArthritis Care &Research, EarlyView.
Objective The study aimed to identify symptom‐based predictors of dry eye disease (DED) signs in the Sjögren's International Collaborative Clinical Alliance (SICCA) cohort. Methods We performed a retrospective analysis examining 16 ocular symptoms (most graded 0–4) and artificial tear (AT) use (graded 0–3) as predictors of DED signs (abnormal ocular ...
Pragnya R. Donthineni   +7 more
wiley   +1 more source

Grand-Canonical Typicality. [PDF]

open access: yesJ Stat Phys
Igelspacher C, Tumulka R, Vogel C.
europepmc   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

open access: yesArthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos   +2 more
wiley   +1 more source

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

open access: yesArthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah   +4 more
wiley   +1 more source

The large deviation principle for stochastic processes. II

open access: yesТеория вероятностей и ее применения, 2002
Miguel A Arcones, Miguel A Arcones
openaire   +3 more sources

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

open access: yesArthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein   +3 more
wiley   +1 more source

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