Literature DB >> 3773651

An evaluation of clinicians' subjective prior probability estimates.

J G Dolan, D R Bordley, A I Mushlin.   

Abstract

The degree of consensus and the accuracy of subjective prior probability estimates made by 104 clinicians were examined. The clinicians' estimates were compared with objective prior probabilities obtained from published sources and actual patient outcomes. Each clinician made seven estimates based upon written case summaries abstracted from patient records. Consensus was measured by calculating estimate ranges and standard deviations. The clinicians' estimates varied widely: the smallest range was 80 (2%-82%); four of the seven probability ranges were greater than 90. The average standard deviation was 19.5. Using these prior probabilities and Bayes' theorem, widely varying posttest probabilities would result after many common diagnostic tests. Accuracy was measured using the Brier score, which ranges from 0 to 1; a score of 0 indicates perfect accuracy. The clinicians' Brier scores ranged from 0.05 to 0.57. The objectively determined probabilities achieved a Brier score of 0.11, better than that of 96% of the clinicians. Clinical experience did not consistently affect estimate accuracy or consensus. The clinicians' subjective estimates were inaccurate measures of the prior probability of disease. There was little consensus regarding disease likelihood among the clinicians. Objective prior probabilities were more accurate and less variable.

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Year:  1986        PMID: 3773651     DOI: 10.1177/0272989X8600600406

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  21 in total

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5.  A formula for estimating pretest probability: evaluation and clinical application.

Authors:  N M Gayed; D E Kern
Journal:  J Gen Intern Med       Date:  1990 Jul-Aug       Impact factor: 5.128

6.  Practice Variation Among Hospitals in Revascularization Therapy and Its Association With Procedure-related Mortality.

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7.  Significance testing as perverse probabilistic reasoning.

Authors:  M Brandon Westover; Kenneth D Westover; Matt T Bianchi
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8.  Could our pretest probabilities become evidence based? A prospective survey of hospital practice.

Authors:  W Scott Richardson; Walter A Polashenski; Brett W Robbins
Journal:  J Gen Intern Med       Date:  2003-03       Impact factor: 5.128

9.  A new method for determining physician decision thresholds using empiric, uncertain recommendations.

Authors:  Michael V Boland; Harold P Lehmann
Journal:  BMC Med Inform Decis Mak       Date:  2010-04-08       Impact factor: 2.796

10.  Expert prior elicitation and Bayesian analysis of the Mycotic Ulcer Treatment Trial I.

Authors:  Catherine Q Sun; N Venkatesh Prajna; Tiruvengada Krishnan; Jeena Mascarenhas; Revathi Rajaraman; Muthiah Srinivasan; Anita Raghavan; Kieran S O'Brien; Kathryn J Ray; Stephen D McLeod; Travis C Porco; Nisha R Acharya; Thomas M Lietman
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-06-14       Impact factor: 4.799

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