Clinical research in carpal tunnel syndrome has been hampered by the lack of a gold standard for the diagnosis. This results in uncertainty in the diagnosis causing problems in diagnostic test research and in other forms of clinical research where it may not be clear if patients actually have carpal tunnel syndrome. The purpose of this poster is to discuss two methods we are exploring to circumvent the need for a gold standard in carpal tunnel research. The latent class method is a mathematical technique to make the best estimate for the sensitivity and specificity of diagnostic tests using actual test data without the need for a gold standard. We have performed this method on a sample of patients with suspected carpal tunnel syndrome and calculated higher values for sensitivity and specificity than presented in the literature. The second method, use of the expanded form of Bayes' theorem for distributions uses a probability distribution for the diagnosis and does not require a gold standard to update the probability of disease. This can be applied to carpal tunnel syndrome by using the Katz hand diagram. The sensitivity and specificity, characteristics that traditionally have required a gold standard are replaced by conditional distributions of positive or negative test results. We and others have measured these distributions directly in patient populations. Future elaboration of these methods offer promise to break the current impasse in carpal tunnel syndrome diagnostic test research.