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Amanda M. VanDenburgh,
Susan Abu-Shakra,
Mitchell F. Brin,
Frederick Beddingfield,
Ilya Lipkovich,
John P. Houston,
Jonna Ahl,
Sara Kollack-Walker,
Bruce Kinon,
Virginia Stauffer, [......],
Mark Batshaw,
Mendel Tuchman,
Robin Elliott,
Veronica Todaro,
Robert G. Robinson,
Ricardo E. Jorge,
Sergio E. Starkstein,
D. Badman,
T. Jackson,
T. Miller
Neurotherapeutics 04/2012; 4(4):725-731. · 6.01 Impact Factor
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Jeffrey M Statland,
Yunxia Wang, Rachel Richesson,
Brian Bundy,
Laura Herbelin,
Joe Gomes,
Jaya Trivedi,
Shannon Venance,
Anthony Amato,
Michael Hanna,
Robert Griggs,
Richard J Barohn
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ABSTRACT: Non-dystrophic myotonia (NDM) is caused by mutations in muscle chloride and sodium channels. Currently, there is no standardized instrument for documenting symptom frequency and severity in NDM.
Subjects used an automated, interactive, telephone-based voice response diary (IVR) to record frequency and severity of stiffness, weakness, pain, and tiredness once a week for 8 weeks, after their baseline visits.
We describe the IVR and report data on 76 subjects for a total of 385 person-weeks. Overall there were 5.1 calls per subject. Forty-eight subjects called in 5 or more times, and 14 called in 8 times. Stiffness was both the most frequent and severe symptom. Warm-up and handgrip myotonia were associated with higher severity scores for stiffness.
IVR is a convenient technology to allow patient reporting of repeated and real-time symptom frequency and severity, and it is presently being used in a trial of mexiletine in NDM.
Muscle & Nerve 07/2011; 44(1):30-5. · 2.37 Impact Factor
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[show abstract]
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ABSTRACT: Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representation that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of these aspects (expression language, codification of eligibility concepts, and patient data modeling) to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward knowledge representation for sharable and computable eligibility criteria.
Journal of Biomedical Informatics 06/2010; 43(3):451-67. · 1.79 Impact Factor
