title: Questionnaire tools for the diagnosis of carpal tunnel syndrome from the patient history.
abstract: BACKGROUND
  There remains no "gold standard" for the diagnosis of carpal tunnel syndrome (CTS). Clinical diagnosis is often held to be paramount but depends on the skills of the individual practitioner. We describe two mathematical approaches to the analysis of a history obtained by questionnaire.
  
  
  METHODS
  We used two earlier instruments, a conventional logistic regression analysis, and an artificial neural network to analyze data from 5860 patients referred for diagnosis of hand symptoms. We evaluated their ability to predict whether nerve conduction studies would show evidence of CTS using receiver operating characteristic curves.
  
  
  RESULTS
  Both new instruments outperformed the existing tools, achieving sensitivity of 88% and specificity of 50% in predicting abnormal median nerve conduction. When combined, 96% sensitivity and 50% specificity were achieved.
  
  
  CONCLUSIONS
  The combined instrument can be used as a preliminary screening tool for CTS, for self-diagnosis, and as a supplement to diagnosis in primary care.
authors: EEG Department, NHS Trust East Kent Hospitals University, Ethelbert Road, Canterbury, Kent CT1 3NG, UK. jeremy.bland@ekht.nhs.uk
authors: Jeremy D P
authors: JD
authors: Bland
authors: Peter
authors: P
authors: Weller
authors: Stephan
authors: S
authors: Rudolfer
mesh_terms: D002349:Carpal Tunnel Syndrome / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D016015:Logistic Models; D008487:Medical History Taking / Q000379:methods / Q000592:standards*; D016571:Neural Networks, Computer; D009460:Neurologic Examination / Q000379:methods / Q000592:standards; D011795:Surveys and Questionnaires / Q000592:standards*