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Uncertain Pretest Probabilities in Diagnostic Reasoning: The Prevalence Threshold as a Tipping Point.

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Τίτλος: Uncertain Pretest Probabilities in Diagnostic Reasoning: The Prevalence Threshold as a Tipping Point.
Συγγραφείς: Nadler M; Department of Psychology, Faculty of Science, McGill University, Montreal, Quebec, Canada., Balayla J; Department of Obstetrics and Gynecology, McGill University, Montreal, Quebec, Canada.; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
Πηγή: Journal of evaluation in clinical practice [J Eval Clin Pract] 2026 Sep; Vol. 32 (6), pp. e70582.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 9609066 Publication Model: Print Cited Medium: Internet ISSN: 1365-2753 (Electronic) Linking ISSN: 13561294 NLM ISO Abbreviation: J Eval Clin Pract Subsets: MEDLINE
Imprint Name(s): Original Publication: Oxford, England : Wiley-Blackwell, c1995-
Ιατρικοί όροι (MeSH): Clinical Decision-Making*/methods , Clinical Reasoning*, Humans ; Uncertainty ; Probability ; Prevalence
Περίληψη: Rationale: Post-test 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-how strongly is a change in the pretest estimate carried through to the post-test probability-apart from the clinical question of whether the remaining uncertainty is wide enough to alter management.
Aims and Objectives: To show that the prevalence threshold ( INLINEMATH ) 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.
Method: Conceptual and analytic study, supported by a targeted narrative overview of pretest-probability estimation and of cognitive influences on diagnostic reasoning. We derived how strongly the positive-result screening curve responds to a change in the pretest probability, examined how that result depends on the scale used, distinguished INLINEMATH from testing and treatment thresholds, and applied the framework to a primary care example.
Results: For a positive likelihood ratio INLINEMATH is the single pretest probability at which one percentage point of pretest uncertainty becomes one percentage point of post-test uncertainty. Below INLINEMATH 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 ( INLINEMATH , the order of magnitude of a positive urinary nitrite), a pretest estimate of 15% gives a post-test probability of 58.5%, and a plausible pretest range of 10%-25% converts exactly to 47.1%-72.7%. That range straddles a 50% action threshold but not a 40% or an 80% one; INLINEMATH itself settles nothing.
Conclusion: The prevalence threshold is a closed-form 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-factors studies with clinicians are needed before clinical implementation.
(© 2026 John Wiley & Sons Ltd.)
References: N Engl J Med. 1978 Nov 2;299(18):999-1001. (PMID: 692627)
Acad Med. 2017 Jan;92(1):23-30. (PMID: 27782919)
BJGP Open. 2023 Sep 19;7(3):. (PMID: 37160336)
CJEM. 2014 Jan;16(1):34-40. (PMID: 24423999)
JAMA Intern Med. 2023 Aug 1;183(8):818-823. (PMID: 37358843)
BMJ Qual Saf. 2014 Sep;23(9):727-31. (PMID: 24742777)
JAMA Intern Med. 2021 Jun 1;181(6):747-755. (PMID: 33818595)
J Gen Intern Med. 2002 Nov;17(11):882-3. (PMID: 12406361)
J Prim Health Care. 2012 Jun 01;4(2):166-73. (PMID: 22675704)
BMC Med Inform Decis Mak. 2016 Nov 3;16(1):138. (PMID: 27809908)
J Clin Epidemiol. 2021 Mar;131:158-160. (PMID: 33741123)
PLoS One. 2020 Oct 7;15(10):e0240215. (PMID: 33027310)
N Engl J Med. 1980 May 15;302(20):1109-17. (PMID: 7366635)
Res Integr Peer Rev. 2019 Mar 26;4:5. (PMID: 30962953)
Med Decis Making. 2018 Jan;38(1):3-13. (PMID: 28649904)
JAMA. 2002 May 22-29;287(20):2701-10. (PMID: 12020306)
BMC Urol. 2004 Jun 02;4:4. (PMID: 15175113)
Diagnosis (Berl). 2026 Apr 24;:. (PMID: 42044307)
N Engl J Med. 1975 Jul 31;293(5):229-34. (PMID: 1143303)
J Clin Epidemiol. 2022 Apr;144:198-202. (PMID: 34740785)
Contributed Indexing: Keywords: Bayes theorem; decision making; diagnostic reasoning; likelihood ratio; pretest probability; prevalence threshold; uncertainty
Entry Date(s): Date Created: 20260908 Date Completed: 20260908 Latest Revision: 20260910
Update Code: 20260910
PubMed Central ID: PMC13552943
DOI: 10.1111/jep.70582
PMID: 42709969
Βάση Δεδομένων: MEDLINE
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  Data: Uncertain Pretest Probabilities in Diagnostic Reasoning: The Prevalence Threshold as a Tipping Point.
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  Data: <searchLink fieldCode="AU" term="%22Nadler+M%22">Nadler M</searchLink>; Department of Psychology, Faculty of Science, McGill University, Montreal, Quebec, Canada.<br /><searchLink fieldCode="AU" term="%22Balayla+J%22">Balayla J</searchLink>; Department of Obstetrics and Gynecology, McGill University, Montreal, Quebec, Canada.; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
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  Data: Rationale: Post-test 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-how strongly is a change in the pretest estimate carried through to the post-test probability-apart from the clinical question of whether the remaining uncertainty is wide enough to alter management.<br />Aims and Objectives: To show that the prevalence threshold ( INLINEMATH ) 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.<br />Method: Conceptual and analytic study, supported by a targeted narrative overview of pretest-probability estimation and of cognitive influences on diagnostic reasoning. We derived how strongly the positive-result screening curve responds to a change in the pretest probability, examined how that result depends on the scale used, distinguished INLINEMATH from testing and treatment thresholds, and applied the framework to a primary care example.<br />Results: For a positive likelihood ratio INLINEMATH is the single pretest probability at which one percentage point of pretest uncertainty becomes one percentage point of post-test uncertainty. Below INLINEMATH 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 ( INLINEMATH , the order of magnitude of a positive urinary nitrite), a pretest estimate of 15% gives a post-test probability of 58.5%, and a plausible pretest range of 10%-25% converts exactly to 47.1%-72.7%. That range straddles a 50% action threshold but not a 40% or an 80% one; INLINEMATH itself settles nothing.<br />Conclusion: The prevalence threshold is a closed-form 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-factors studies with clinicians are needed before clinical implementation.<br /> (© 2026 John Wiley & Sons Ltd.)
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  Data: N Engl J Med. 1978 Nov 2;299(18):999-1001. (PMID: <searchLink fieldCode="PM" term="%22692627%22">692627)</searchLink><br />Acad Med. 2017 Jan;92(1):23-30. (PMID: <searchLink fieldCode="PM" term="%2227782919%22">27782919)</searchLink><br />BJGP Open. 2023 Sep 19;7(3):. (PMID: <searchLink fieldCode="PM" term="%2237160336%22">37160336)</searchLink><br />CJEM. 2014 Jan;16(1):34-40. (PMID: <searchLink fieldCode="PM" term="%2224423999%22">24423999)</searchLink><br />JAMA Intern Med. 2023 Aug 1;183(8):818-823. (PMID: <searchLink fieldCode="PM" term="%2237358843%22">37358843)</searchLink><br />BMJ Qual Saf. 2014 Sep;23(9):727-31. (PMID: <searchLink fieldCode="PM" term="%2224742777%22">24742777)</searchLink><br />JAMA Intern Med. 2021 Jun 1;181(6):747-755. (PMID: <searchLink fieldCode="PM" term="%2233818595%22">33818595)</searchLink><br />J Gen Intern Med. 2002 Nov;17(11):882-3. (PMID: <searchLink fieldCode="PM" term="%2212406361%22">12406361)</searchLink><br />J Prim Health Care. 2012 Jun 01;4(2):166-73. (PMID: <searchLink fieldCode="PM" term="%2222675704%22">22675704)</searchLink><br />BMC Med Inform Decis Mak. 2016 Nov 3;16(1):138. (PMID: <searchLink fieldCode="PM" term="%2227809908%22">27809908)</searchLink><br />J Clin Epidemiol. 2021 Mar;131:158-160. (PMID: <searchLink fieldCode="PM" term="%2233741123%22">33741123)</searchLink><br />PLoS One. 2020 Oct 7;15(10):e0240215. (PMID: <searchLink fieldCode="PM" term="%2233027310%22">33027310)</searchLink><br />N Engl J Med. 1980 May 15;302(20):1109-17. (PMID: <searchLink fieldCode="PM" term="%227366635%22">7366635)</searchLink><br />Res Integr Peer Rev. 2019 Mar 26;4:5. (PMID: <searchLink fieldCode="PM" term="%2230962953%22">30962953)</searchLink><br />Med Decis Making. 2018 Jan;38(1):3-13. (PMID: <searchLink fieldCode="PM" term="%2228649904%22">28649904)</searchLink><br />JAMA. 2002 May 22-29;287(20):2701-10. (PMID: <searchLink fieldCode="PM" term="%2212020306%22">12020306)</searchLink><br />BMC Urol. 2004 Jun 02;4:4. (PMID: <searchLink fieldCode="PM" term="%2215175113%22">15175113)</searchLink><br />Diagnosis (Berl). 2026 Apr 24;:. (PMID: <searchLink fieldCode="PM" term="%2242044307%22">42044307)</searchLink><br />N Engl J Med. 1975 Jul 31;293(5):229-34. (PMID: <searchLink fieldCode="PM" term="%221143303%22">1143303)</searchLink><br />J Clin Epidemiol. 2022 Apr;144:198-202. (PMID: <searchLink fieldCode="PM" term="%2234740785%22">34740785)</searchLink>
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