Trial reportJournal of general internal medicine2011
Does prevalence matter to physicians in estimating post-test probability of disease? A randomized trial.
Trial report in Journal of general internal medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How well do health professionals interpret diagnostic information? A systematic review.BMJ open · 2015Pooled it
- A comprehension scale for central-line associated bloodstream infection: Results of a preliminary survey and factor analysis.PloS one · 2018Trial
- A Randomised Assessment of Trainee Doctors' Understanding and Interpretation of Diagnostic Test Results.World journal of surgery · 2016Trial
- Trial
- Doctors and patients' susceptibility to framing bias: a randomized trial.Journal of general internal medicine · 2011Trial
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- Influence of risk literacy, decision-making styles and motivation on clinical reasoning in medical students: an ordinal logistic regression analysis.BMC medical education · 2025Article
- Interpretation of results of PCR and B-D-glucan for the diagnosis of Pneumocystis Jirovecii Pneumonia in immunocompromised adults with acute respiratory failure.Annals of intensive care · 2024Article
- Family physicians overestimate diagnosis probabilities regardless of the test results.Frontiers in medicine · 2023Article
- Article
- Review
- Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting.Diagnostics (Basel, Switzerland) · 2019Article
- Development and Validation of a Mucosal Impedance Contour Analysis System to Distinguish Esophageal Disorders.Gastroenterology · 2019Article
- Potential for diagnosis of infectious disease from the 100,000 Genomes Project Metagenomic Dataset: Recommendations for reporting results.Wellcome open research · 2019Article
- Review of the use of pretest probability for molecular testing in non-small cell lung cancer and overview of new mutations that may affect clinical practice.Therapeutic advances in medical oncology · 2017Review
- Do Clinicians Understand Quality Metric Data? An Evaluation in a Twitter-Derived Sample.Journal of hospital medicine · 2017Article
- The Challenge of Latent TB Infection.JAMA · 2016Article
- Bayesian reasoning in residents' preliminary diagnoses.Cognitive research: principles and implications · 2016Article
- Effect of numbering of return envelopes on participation, explicit refusals, and bias: experiment and meta-analysis.BMC medical research methodology · 2014Article
- A high resolution computer tomography scoring system to predict culture-positive pulmonary tuberculosis in the emergency department.PloS one · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known.
objectiveTo assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups.
designRandomized trial.
participantsPopulation-based sample of 1,361 physicians of all clinical specialties.
interventionWe described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information). MAIN MEASURES: We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60-79%, 80-94%, 95-99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the "no information" scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing. KEY
resultsMost respondents in each of the six groups (67%-82%) selected a post-test probability of 95-99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0-14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting.
conclusionsMost physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors.
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