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Johns Hopkins HealthCare redcap electronic data capture tools
Redcap Electronic Data Capture Tools, supplied by Johns Hopkins HealthCare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/electronic+data+capture+tools/redcap+electronic+data+capture+tools/pm40664545-76-8-16
Average 90 stars, based on 1 article reviews
redcap electronic data capture tools - by Bioz Stars, 2026-10
90/100 stars

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Article Title: Wakȟáŋyeža (Little Holy One) - an intergenerational intervention for Native American parents and children: a protocol for a randomized controlled trial with embedded single-case experimental design
Article Snippet: Allocation will be handled by Research Electronic Data Capture (REDCap) electronic data capture tools hosted at Johns Hopkins University [ , ].

Article Title: Long-term renal function in living kidney donors with simple renal cysts: A retrospective cohort study
Article Snippet: Data collection and management Study data were collected and managed using REDCap electronic data capture tools hosted at Johns Hopkins University.

Article Title: The use of ultrasound in the search for the primary site of unknown primary head and neck squamous cell cancers
Article Snippet: Study data were collected and managed using REDCap (Research Electronic Data Capture), an electronic data capture tools hosted by Johns Hopkins Bloomberg School of Public Health.

Article Title: Digitoxin for Airway Inflammation in Cystic Fibrosis: Preliminary Assessment of Safety, Pharmacokinetics, and Dose Finding
Article Snippet: Study data were collected and managed using Research Electronic Data Capture (REDCap) electronic data capture tools hosted at the Johns Hopkins University Bloomberg School of Public Health.

Article Title: Machine Learning Models for Prediction of Diabetic Microvascular Complications.
Article Snippet: Importance and Aims: Diabetic microvascular complications significantly impact morbidity and mortality.. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).. Methods: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications.

Imaging:

Article Title: Clinical Presentation and Stroke Incidence Differ by Moyamoya Etiology.
Article Snippet: Introduction: Moyamoya arteriopathy, which can be idiopathic or associated with sickle cell disease, neurofibromatosis, Down syndrome, or cranial radiation therapy, is a progressive cerebral arteriopathy associated with high rates of incident and recurrent stroke.. Little is known about how these subgroups differ with respect to clinical presentation, radiographic findings, stroke risk, and functional outcomes.. Methods: Using ICD codes, we identified children ages 28 days to 18 years treated for moyamoya arteriopathy at our tertiary care center between 2003 and 2019.



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