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Objective: To review the nature of the presentation of neurosyphilis, the value of diagnostic tests, and the classification of the disease. Methods: A retrospective review was carried out of the records of patients who had been identified as possible cases of neurosyphilis by a positive FTA-abs test in the CSF. The review extended over 10 years at a single hospital which served a population of mixed ancestry in a defined catchment area in the Western Cape province of South Africa. Patients were placed in predefined diagnostic categories, and clinical, radiological, and laboratory features were assessed. Results: 161 patients met diagnostic criteria for neurosyphilis: 82 presented with combinations of delirium and dementia and other neuropsychiatric conditions, and the remainder had typical presentations such as stroke (24), spinal cord disease (15), and seizures (14). The average age of presentation ranged from 35.9 to 42.6 years in the different categories of neurosyphilis. Of those followed up, 77% had residual deficits from their initial illness. Cerebrospinal fluid (CSF) VDRL was positive in 73% of cases. Conclusions: The diagnosis of neurosyphilis can be made with reasonable certainty if there is an appropriate neuropsychiatric syndrome associated with a positive CSF VDRL. If the VDRL is negative, a positive FTA-abs in an appropriate clinical setting, associated with raised CSF cell count, protein, or IgG index, is a useful method of identifying neurosyphilis. Tabes dorsalis has become uncommon, but this is likely to be the only manifestation of neurosyphilis that has been altered during the antibiotic era.

Background: Statistical models that predict functional outcome after stroke using six simple variables (SSV) have recently been developed and validated. Objective: To compare the accuracy of these models with other simple ways of predicting outcome soon after stroke. Methods: The SSV model for being alive and independent (modified Rankin score ⩽2) six months or one year after stroke was compared with predictions based on a model that included only age and Oxford community stroke project classification, with predictions based on conscious level and urinary continence, and with informal clinical predictions made by clinicians interested in stroke. Predictions were compared in an independent hospital based cohort of stroke patients using receiver operator characteristic (ROC) curves. Results: The SSV model at six months had a significantly greater area under the curve (0.84) than the model with only age and stroke classification (0.75). Predictions based on conscious level and urinary continence were no better than those of the SSV model and were unable to predict subjects with a high probability of good outcome. The sensitivity and specificity for informal clinical predictions at one year lay on or below the SSV model curve, implying that the SSV model was at least as good as clinical predictions. Conclusions: The SSV models performed as well as or better than other simple predictive systems. These models will be useful in epidemiological studies but should not be used to guide clinical management until their impact on patient care and outcome has been evaluated.