We describe a classifier-enhanced nearest neighbor approach to assigning Medical Subject Headings (MeSH®) to unlabeled documents using a combination of abstract similarities and direct citations to labeled MEDLINE records. The approach frames the classification problem by decomposing it into sets of siblings in the MeSH hierarchy (e.g., training a classifier for predicting 'Heterocyclic Compounds, 2-Ring' vs. other 'Heterocyclic Compounds'). Preliminary experiments using a small but diverse set of MeSH terms shows the highest performance when using both abstracts and citations compared to each alone, and coupled with a non-naive classifier: 90+% precision and recall with 10fold cross-validation. NLM's Medical Text Indexer (MTI) tool achieves similar overall performance but varies more across the terms tested. For example, MTI performs better on 'Heterocyclic Compounds, 2-Ring', while our approach performs better on Alzheimer Disease and Neuroimaging. Our approach can be applied broadly to documents with abstracts that are similar to (or cite) MEDLINE abstracts, which would help linking and searching across bibliographic databases beyond MEDLINE.