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An appreciation of prognosis is essential for effective clinical practice, in neurology as in other fields. Predicting risk of a poor outcome in individuals, or at least in well-defined groups of individuals, is necessary to properly inform patients about what the future holds for them, and the likely benefits of treatment. Despite the various pitfalls in the derivation and validation of prognostic models (or scores), there are an increasing number of useful models available to help patients and clinicians in routine neurology practice. This article considers how such models are best derived, and how their reliability should be assessed, drawing on examples from various different neurological disorders.

Original publication




Journal article


Pract Neurol

Publication Date





242 - 253


Humans, Models, Statistical, Nervous System Diseases, Neurology, Patient Selection, Predictive Value of Tests, Prognosis, Reproducibility of Results, Risk Assessment, Risk Factors