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A Web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis.

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Title: A Web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis.
Authors: Galea, I., Lederer, C., Neuhaus, A., Muraro, P. A., Scalfari, A., Koch ‐ Henriksen, N., Heesen, C., Koepke, S., Stellmann, P., Albrecht, H., Winkelmann, A., Weber, F., Bahn, E., Hauser, M., Edan, G., Ebers, G., Daumer, M.
Source: European Journal of Neurology. Jul2013, Vol. 20 Issue 7, p1107-1109. 3p. 2 Graphs.
Subjects: Multiple sclerosis, Disease progression, Neurologists, Prognosis, Field research, Health counseling, Patients
Abstract: Background and purpose The Evidence-Based Decision Support Tool in Multiple Sclerosis ( EBDiMS) is the first Web-based prognostic calculator in multiple sclerosis ( MS) capable of delivering individualized estimates of disease progression. It has recently been extended to provide long-term predictions based on the data from a large natural history cohort. Methods We compared the predictive accuracy and consistency of EBDiMS with that of 17 neurologists highly specialized in MS. Results We show that whilst the predictive accuracy was similar, neurologists showed a significant intra-rater and inter-rater variability. Conclusions Because EBDiMS was consistent, it is of superior utility in a specialist setting. Further field testing of EBDiMS in non-specialist settings, and investigation of its usefulness for counselling patients in treatment decisions, is warranted. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: A Web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis.
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  Data: <searchLink fieldCode="AR" term="%22Galea%2C+I%2E%22">Galea, I.</searchLink><br /><searchLink fieldCode="AR" term="%22Lederer%2C+C%2E%22">Lederer, C.</searchLink><br /><searchLink fieldCode="AR" term="%22Neuhaus%2C+A%2E%22">Neuhaus, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Muraro%2C+P%2E+A%2E%22">Muraro, P. A.</searchLink><br /><searchLink fieldCode="AR" term="%22Scalfari%2C+A%2E%22">Scalfari, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Koch+‐+Henriksen%2C+N%2E%22">Koch ‐ Henriksen, N.</searchLink><br /><searchLink fieldCode="AR" term="%22Heesen%2C+C%2E%22">Heesen, C.</searchLink><br /><searchLink fieldCode="AR" term="%22Koepke%2C+S%2E%22">Koepke, S.</searchLink><br /><searchLink fieldCode="AR" term="%22Stellmann%2C+P%2E%22">Stellmann, P.</searchLink><br /><searchLink fieldCode="AR" term="%22Albrecht%2C+H%2E%22">Albrecht, H.</searchLink><br /><searchLink fieldCode="AR" term="%22Winkelmann%2C+A%2E%22">Winkelmann, A.</searchLink><br /><searchLink fieldCode="AR" term="%22Weber%2C+F%2E%22">Weber, F.</searchLink><br /><searchLink fieldCode="AR" term="%22Bahn%2C+E%2E%22">Bahn, E.</searchLink><br /><searchLink fieldCode="AR" term="%22Hauser%2C+M%2E%22">Hauser, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Edan%2C+G%2E%22">Edan, G.</searchLink><br /><searchLink fieldCode="AR" term="%22Ebers%2C+G%2E%22">Ebers, G.</searchLink><br /><searchLink fieldCode="AR" term="%22Daumer%2C+M%2E%22">Daumer, M.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neurology%22">European Journal of Neurology</searchLink>. Jul2013, Vol. 20 Issue 7, p1107-1109. 3p. 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+sclerosis%22">Multiple sclerosis</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+progression%22">Disease progression</searchLink><br /><searchLink fieldCode="DE" term="%22Neurologists%22">Neurologists</searchLink><br /><searchLink fieldCode="DE" term="%22Prognosis%22">Prognosis</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br /><searchLink fieldCode="DE" term="%22Health+counseling%22">Health counseling</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink>
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  Data: Background and purpose The Evidence-Based Decision Support Tool in Multiple Sclerosis ( EBDiMS) is the first Web-based prognostic calculator in multiple sclerosis ( MS) capable of delivering individualized estimates of disease progression. It has recently been extended to provide long-term predictions based on the data from a large natural history cohort. Methods We compared the predictive accuracy and consistency of EBDiMS with that of 17 neurologists highly specialized in MS. Results We show that whilst the predictive accuracy was similar, neurologists showed a significant intra-rater and inter-rater variability. Conclusions Because EBDiMS was consistent, it is of superior utility in a specialist setting. Further field testing of EBDiMS in non-specialist settings, and investigation of its usefulness for counselling patients in treatment decisions, is warranted. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Neurologists
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