Objective: To use patient data to evaluate and construct diagnostic criteria for inclusion body myositis (IBM), a progressive disease of skeletal muscle. Methods: The literature was reviewed to identify all previously proposed IBM diagnostic criteria. These criteria were applied through medical records review to 200 patients diagnosed as having IBM and 171 patients diagnosed as having a muscle disease other than IBM by neuromuscular specialists at 2 institutions, and to a validating set of 66 additional patients with IBMfrom2 other institutions. Machine learning techniques were used for unbiased construction of diagnostic criteria. Results: Twenty-four previously proposed IBM diagnostic categories were identified. Twelve categories all performed with high (≥97%) specificity but varied substantially in their sensitivities (11%-84%). The best performing category was European Neuromuscular Centre 2013 probable (sensitivity of 84%). Specialized pathologic features and newly introduced strength criteria (comparative knee extension/hip flexion strength) performed poorly. Unbiased data-directed analysis of 20 features in 371 patients resulted in construction of higher-performing data-derived diagnostic criteria (90% sensitivity and 96% specificity). Conclusions: Published expert consensus-derived IBM diagnostic categories have uniformly high specificity but wide-ranging sensitivities. High-performing IBM diagnostic category criteria can be developed directly from principled unbiased analysis of patient data. Classification of evidence: This study provides Class II evidence that published expert consensus- derived IBM diagnostic categories accurately distinguish IBM from other muscle disease with high specificity but wide-ranging sensitivities. © 2014 American Academy of Neurology.