ArticleBMC medicine2019
Three myths about risk thresholds for prediction models.
Article in BMC medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 7 of them syntheses that pooled it.
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Who cites it
97 citing papers in PubMed, 7 syntheses or guidelines pooled it.
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- Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model.BMJ mental health · 2026Article
- What constitutes high risk for venous thromboembolism? Determining an appropriate threshold to initiate prophylaxis.Journal of thrombosis and haemostasis : JTH · 2026Article
- Preoperative Prediction of Intraoperative Transfusion in Pediatric Craniosynostosis Surgery: An Exploratory Prediction Model Study.Medicina (Kaunas, Lithuania) · 2026Article
- Protecting clinical value judgment in the age of AI.NPJ digital medicine · 2026Article
- Using SCORE2 with a risk chart or online calculator: impact on model performance, treatment eligibility, and cardiovascular disease prevention.European heart journal. Quality of care & clinical outcomes · 2026Article
- Identifying subjects at risk of liver cirrhosis via a range of thresholds for common fibrosis markers: A Welsh general population-based cohort study.Journal of internal medicine · 2026Article
- Deep learning-based mismatch repair prediction using colorectal cancer macroscopic images: a diagnostic study.Journal of gastroenterology · 2026Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical prediction models are useful in estimating a patient's risk of having a certain disease or experiencing an event in the future based on their current characteristics. Defining an appropriate risk threshold to recommend intervention is a key challenge in bringing a risk prediction model to clinical application; such risk thresholds are often defined in an ad hoc way. This is problematic because tacitly assumed costs of false positive and false negative classifications may not be clinically sensible. For example, when choosing the risk threshold that maximizes the proportion of patients correctly classified, false positives and false negatives are assumed equally costly. Furthermore, small to moderate sample sizes may lead to unstable optimal thresholds, which requires a particularly cautious interpretation of results. MAIN TEXT: We discuss how three common myths about risk thresholds often lead to inappropriate risk stratification of patients. First, we point out the contexts of counseling and shared decision-making in which a continuous risk estimate is more useful than risk stratification. Second, we argue that threshold selection should reflect the consequences of the decisions made following risk stratification. Third, we emphasize that there is usually no universally optimal threshold but rather that a plausible risk threshold depends on the clinical context. Consequently, we recommend to present results for multiple risk thresholds when developing or validating a prediction model.
conclusionBearing in mind these three considerations can avoid inappropriate allocation (and non-allocation) of interventions. Using discriminating and well-calibrated models will generate better clinical outcomes if context-dependent thresholds are used.
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