ArticleBMC research notes2016
The clinical utility curve: a proposal to improve the translation of information provided by prediction models to clinicians.
Article in BMC research notes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Understanding Clinicians' Informational Needs for AI-Driven Clinical Decision Support Systems: Qualitative Interview Study.JMIR medical education · 2026Article
- Predictability of peripheral blood inflammatory markers in prediction of lymph node metastasis in gastric cancer.Therapeutic advances in gastroenterology · 2026Article
- Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach.Renal failure · 2025Article
- Early detection of positive urine culture in patients with urolithiasis: a machine learning model with dynamic online nomogram.Annals of medicine · 2025Article
- Article
- Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.Renal failure · 2024Article
- Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma.Frontiers in endocrinology · 2022Article
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Authors and funding
1 author.
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Abstract
backgroundPrediction models are essential to the development of prediction rules that guide decision-making, and comparison of prediction models with and without an additional diagnostic or prognostic risk factor allows assessment of the value of the additional factor in risk prediction. However, the many different measures described to translate the information provided by a prediction model do not readily assist clinicians' decision-making.
resultsThe clinical utility (CU) curve is proposed as an alternative method of communication of information from a prediction model to the clinician. The CU curve is essentially a derivation of the ROC curve that has sensitivity on the y-axis and the number needed to capture one case (NNCOC) on the x-axis. It provides information about the relationship between sensitivity and false positive rate over the full range of prediction score thresholds, and it also indicates the proportion of the patient population with an absolute risk below the threshold for 100% sensitivity that can therefore be classified as free of disease.
conclusionsThe CU curve is proposed as a means to assist the translation of model information to the clinician, in the hope that it will stimulate debate and, through refinement, assist the development of prediction rules with optimal clinical utility.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.