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The McDonald criteria maintained a scheme for diagnosing MS based solely on clinical grounds but also proposed for the first time that when clinical evidence is lacking, magnetic resonance imaging (MRI) findings can serve as surrogates for dissemination in space (DIS) and/or time (DIT) to diagnose MS. [5] The criteria try to prove the existence ...
Since the first description of multiple sclerosis (MS) by Charcot, the Neurological community has been striving to create reliable and reproducible criteria for diagnosis of MS. [32] The first attempts were made by Charcot himself, followed by Marburg and later Allison. The first criteria however were lacking in sensitivity and specificity for ...
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Historically, the first widespread set of criteria were the Schumacher criteria (also spelled sometimes Schumacker). Currently, testing of cerebrospinal fluid obtained from a lumbar puncture can provide evidence of chronic inflammation of the central nervous system, looking for oligoclonal bands of IgG on electrophoresis, which are inflammation markers found in 75–85% of people with MS., [2 ...
Dental Admission Test (DAT) – (United States) Graduate Management Admission Test (GMAT) – Graduate Record Examination (GRE) – (US and Canada) Law School Admission Test (LSAT) – (US and Canada) Miller Analogies Test (MAT) Medical College Admission Test (MCAT) – (US and Canada) Optometry Admission Test (OAT) – Optometry Admission Test
Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1]The choice of the test depends on many properties of the research question.
The purpose of the comparison is to determine which candidate model is most appropriate for statistical inference. Common criteria for comparing models include the following: R 2, Bayes factor, and the likelihood-ratio test together with its generalization relative likelihood. For more on this topic, see statistical model selection.
In statistics, the Hannan–Quinn information criterion (HQC) is a criterion for model selection. It is an alternative to Akaike information criterion (AIC) and Bayesian information criterion (BIC). It is given as = + ( ()),