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My concern here is that you have over-fitted the data or have perfect separation. It means that the data used to build the model will be fit perfectly by the model but that new cases might be predicted very badly. For any model with say 50 cases and 50 predictors you will get 100% fit. You can either hold some sample back (for testing) or use a more sophisticated method like Correlated Component Regression designed for high dimensional logistic problems (number of predictors approaching or exceeding the number of cases) which does the crossvalidation as part of the model selection process.