Academic Journal
Uncertain Pretest Probabilities in Diagnostic Reasoning: The Prevalence Threshold as a Tipping Point.
| 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 |
| ISSN: | 1365-2753 |
|---|---|
| DOI: | 10.1111/jep.70582 |