Drug Side Effect Profiles Vary in Rheumatoid, Psoriatic Arthritis

2008 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
DENISE NAPOLI
2021 ◽  
Vol 111 (4) ◽  
Author(s):  
James A. Wright ◽  
Jessica A. Wenz ◽  
Gabrielle Jackson Madrigal

Triamcinolone acetonide is a synthetic glucocorticoid used to treat numerous acute and chronic inflammatory conditions. The various side effects of this drug from parenteral administration are well documented in the literature. In this study, three patients present with a rare side effect of violaceous dermal pigmentation. To the best of the authors' knowledge, this finding is rarely presented in the current literature. The purpose of this study is to provide awareness of a less-documented, delayed side effect from triamcinolone acetonide administration. Although all patients presenting in this study had a known history of autoimmune disease (eg, lupus, psoriatic arthritis) further research is needed to suggest a possible association between dermal violaceous change and the use of triamcinolone.


2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Emmanuel Bresso ◽  
Renaud Grisoni ◽  
Gino Marchetti ◽  
Arnaud Sinan Karaboga ◽  
Michel Souchet ◽  
...  

2011 ◽  
Vol 12 (1) ◽  
pp. 169 ◽  
Author(s):  
Edouard Pauwels ◽  
Véronique Stoven ◽  
Yoshihiro Yamanishi
Keyword(s):  

1992 ◽  
Vol 22 (3) ◽  
pp. 787-797 ◽  
Author(s):  
Alec Buchanan

SynopsisThe study is a prospective investigation of the factors associated with treatment compliance in 61 patients discharged from hospital with a ward diagnosis of schizophrenia. All cases were classified using reliable diagnostic criteria and all were followed up for two years. Compliance was assessed by inspection of records and by analysis of urine. Sociodemographic factors and illness variables were unrelated to compliance. Some aspects of a patient's insight and attitude, namely, a belief that medication had helped during the admission, a stated willingness to take treatment after discharge and a generally optimistic outlook, were associated with improved compliance. Other variables which showed such an association were the absence of the drug side-effect akinesia, good previous compliance and voluntary, as opposed to compulsory, admission to hospital.


2018 ◽  
Vol 25 (10) ◽  
pp. 1339-1350 ◽  
Author(s):  
Justin Mower ◽  
Devika Subramanian ◽  
Trevor Cohen

Abstract Objective The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring. Methods Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug/side-effect reference set were evaluated against a list of ≈1100 drugs from an online database. Results The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.95; F1 0.84 across the two sets), outperforming methods utilizing literature and/or spontaneous reporting system data. Examination of predictions for unseen drug/side-effect pairs indicates the ability of these methods to generalize, with over tenfold label support enrichment in the top 100 predictions versus the bottom 100 predictions. Discussion and Conclusion Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.


Sign in / Sign up

Export Citation Format

Share Document