Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial

Table 3 Study population (training data set, n = 210; prospective data of known diagnoses, n = 64)

Diagnostic group	Number of questionnaires (retrospective)	Number of questionnaires (prospective)	Number of questionnaires (total)
Diagnosis 1 (muscular dystrophy/myotonia, MdMy)^a	50	10	60
Diagnosis 2 (Pompe disease, MP)	43	2	45
Diagnosis 3 (spinal muscular atrophy, SMA)	16	4	20
Diagnosis 4 (amyotrophic lateral sclerosis, ALS)	27	17	44
Diagnosis 5 (polyneuropathy, PNP)	22	23	45
Diagnosis 6 (other neuromuscular diseases, OND)^b	16	8	24
Diagnosis 7 (no neuromuscular disease, NND)	36	0	36
Total	210	64	274

^aIncluding patients with Duchenne and Becker muscular dystrophy (MD), oculopharyngeal muscular dystrophy (OPMD), proximal myotonic myopathy (PROMM), facioscapulohumeral MD, progressive MD, limb-girdle-MD, myotonia congenita Thomsen
^bIncluding patients with chronic progressive external opthalmoplegia (CPEO)-plus, polymyositis, Ullrich congenital muscular dystrophy, Miyoshi myopathy, Friedreich ataxia, primary lateral sclerosis (PLS), spinal and bulbar muscular atrophy (SBMA)

ISSN: 1472-6947