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This paper investigates multi-topic aspects in automatic classification of clinical free text. In many practical situ- ations, we need to deal with documents overlapping with multiple topics. Automatic assignment of multiple ICD-9- CM codes to clinical free text in medical records is a typi- cal multi-topic text classification problem. In this paper, we facilitate two different views on multi-topics. The Closed Topic Assumption (CTA) regards an absence of topics for a document as an explicit declaration that this document does not belong to those absent topics. In contrast, the Open Topic Assumption (OTA) considers the missing topics as neutral topics. This paper compares performances of vari- ous interpretations of a multi-topic Text Classification prob- lem into a Machine Learning problem. Experimental results show that the characteristics of multi-topic assignments in the Medical NLP Challenge data is OTA-oriented.