{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T13:00:41Z","timestamp":1790427641873,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Evaluation Clinical Practice"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Rationale<\/jats:title>\n                    <jats:p>Post\u2010test probabilities are reported as precise numbers even though the pretest probabilities they depend on are uncertain and, for an individual patient, unobservable. Clinicians need a way to tell the mathematical question\u2014how strongly is a change in the pretest estimate carried through to the post\u2010test probability\u2014apart from the clinical question of whether the remaining uncertainty is wide enough to alter management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims and Objectives<\/jats:title>\n                    <jats:p>To show that the prevalence threshold () marks the tipping point at which uncertainty expressed in percentage points is passed on unchanged by a positive test result, and to combine that result with exact conversion of pretest ranges and with the clinician's own action threshold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Conceptual and analytic study, supported by a targeted narrative overview of pretest\u2010probability estimation and of cognitive influences on diagnostic reasoning. We derived how strongly the positive\u2010result screening curve responds to a change in the pretest probability, examined how that result depends on the scale used, distinguished  from testing and treatment thresholds, and applied the framework to a primary care example.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For a positive likelihood ratio  is the single pretest probability at which one percentage point of pretest uncertainty becomes one percentage point of post\u2010test uncertainty. Below  such changes are magnified; above it they are damped. The tipping point exists only when uncertainty is measured in percentage points, because a positive result multiplies the odds by the same factor at every pretest probability. In a worked primary care example (, the order of magnitude of a positive urinary nitrite), a pretest estimate of 15% gives a post\u2010test probability of 58.5%, and a plausible pretest range of 10%\u201325% converts exactly to 47.1%\u201372.7%. That range straddles a 50% action threshold but not a 40% or an 80% one;  itself settles nothing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The prevalence threshold is a closed\u2010form description of how uncertainty travels through a positive test result, not a decision rule and not a guarantee of precision. Its useful role is to prompt clinicians to state a pretest range, convert it, and compare the result with the probability at which they would act differently. Vignette and human\u2010factors studies with clinicians are needed before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jep.70582","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T18:42:02Z","timestamp":1788892922000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertain Pretest Probabilities in Diagnostic Reasoning: The Prevalence Threshold as a Tipping Point"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4798-5996","authenticated-orcid":false,"given":"Maya","family":"Nadler","sequence":"first","affiliation":[{"name":"Department of Psychology, Faculty of Science McGill University Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0783-8063","authenticated-orcid":false,"given":"Jacques","family":"Balayla","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology McGill University Montreal Quebec Canada"},{"name":"Lady Davis Institute for Medical Research Jewish General Hospital Montreal Quebec 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