title: GPs' and physicians' interpretation of risks, benefits and diagnostic test results.
abstract: BACKGROUND
  Understanding pre-test probability and baseline risks helps to interpret the results of diagnostic tests and the benefits of treatment, but how good is the understanding of these concepts?
  
  
  OBJECTIVE
  Our aim was to assess the ability of GPs and consultant physicians to make accurate estimates and understand the application of pre-test probability and baseline risk for two common clinical conditions.
  
  
  METHODS
  A two-stage questionnaire survey based on case scenarios of patients with angina and congestive heart failure was carried out of 202 physicians, randomly selected from the members of the Royal College of Physicians in the NW of England, 205 GPs randomly chosen from the practice list of the NW Health Authorities and 128 MRCGP examiners attending an examiners meeting. A total of 115, 106 and 81 members of these groups, respectively, responded to the first stage, and 44, 46 and 64 to the second. The main outcome measures were the stated likelihood of true ischaemic heart disease (IHD) being present and the predicted 1-year mortality; the impact of changing prevalence and baseline risk on these results; and interpretation of different methods of risk presentation.
  
  
  RESULTS
  Estimates of pre-test probability of IHD being present ranged from 5 to 100% and of baseline risk of 1-year mortality from 0 to 86%. More GP examiners and consultant physicians understood the impact of increasing age on the test result than did the random sample of GPs. A majority of each group correctly said that increasing age would reduce the number needed to treat (NNT). Presentation of benefit as relative risk reduction was a greater stimulus to starting treatment than the NNT or measures of population impact.
  
  
  CONCLUSIONS
  Clinicians should collect data to allow a better knowledge of the likelihood of disease and of baseline risk in their patient populations. Methods to increase the understanding of the influence of pre-test probability on diagnostic test results and of how to quantify and demonstrate the impact of the benefit of interventions should be explored.
authors.affiliation: Evidence for Population Health Unit, School of Epidemiology and Health Sciences, The Medical School, The University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, UK. Dick.Heller@man.ac.uk
authors.forename: Richard F
authors.initials: RF
authors.lastname: Heller
authors.forename: John E
authors.initials: JE
authors.lastname: Sandars
authors.forename: Lesley
authors.initials: L
authors.lastname: Patterson
authors.forename: Patrick
authors.initials: P
authors.lastname: McElduff
mesh_terms: D002983:Clinical Competence / Q000706:statistics & numerical data*; D003253:Consultants / Q000706:statistics & numerical data*; D004739:England; D005260:Female; D019538:Health Care Surveys; D006801:Humans; D008297:Male; D017202:Myocardial Ischemia / Q000175:diagnosis*; D010821:Physicians, Family / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data