Results 141 to 150 of about 5,191,119 (260)

Real‐World Effectiveness and Safety of Secukinumab in Giant Cell Arteritis: An Italian Multicenter Cohort Study

open access: yesArthritis Care &Research, EarlyView.
Objective The objective of this study was to evaluate the real‐world effectiveness and safety of secukinumab in patients with giant cell arteritis (GCA). Methods This multicenter retrospective study included patients with GCA who received secukinumab at 14 Italian centers with at least six months of follow‐up.
Luca Iorio   +28 more
wiley   +1 more source

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

open access: yesArthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen   +24 more
wiley   +1 more source

Positive Affect is Associated with Better Physical Function in Early Rheumatoid Arthritis

open access: yesArthritis Care &Research, Accepted Article.
Objective This study examined the association between positive affect and physical function in individuals with early rheumatoid arthritis (RA). Methods We analyzed baseline data from 129 adults with early RA (persistent joint symptoms for ≤ 24 months) and active disease enrolled in the Central Pain in RA 2 (CPIRA‐2) study.
Burcu Aydemir   +9 more
wiley   +1 more source

The Variances of Regression Coefficient Estimates Using Aggregate Data [PDF]

open access: yes
This paper considers the effect of aggregation on the variance of parameter estimates for a linear regression model with random coefficients and an additive error term.
Roy E. Welsch, Edwin Kuh
core  

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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