ArticleHealthcare (Basel, Switzerland)2023
Optimizing Clinical Decision Making with Decision Curve Analysis: Insights for Clinical Investigators.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-enabled liquid biopsy in cancer: a systematic review and meta- analysis of diagnostic performance and biological implications.Frontiers in oncology · 2026Pooled it
- Approaches to Incorporation of Preferences into Health Economic Models of Genomic Medicine: A Critical Interpretive Synthesis and Conceptual Framework.Applied health economics and health policy · 2025Pooled it
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Stroke and Large Vessel Occlusion Risk According to Cincinnati Prehospital Stroke Scale Symptom Patterns in a CPSS-Positive EMS Cohort: A Prospective Cohort Study.Journal of clinical medicine · 2026Article
- Key considerations when assessing clinical utility for biomarker testing: A checklist.Journal of managed care & specialty pharmacy · 2026Article
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.Scientific reports · 2026Article
- Incremental Predictive Value of the Non-HDL-C/HDL-C Ratio for Cryptogenic Stroke in Patients with Patent Foramen Ovale.Journal of cardiovascular development and disease · 2026Article
- Exploring the prevalence and clinical impact of carotid plaque burden by Doppler ultrasound in lung cancer screening participants with limited coronary artery calcification.European radiology · 2026Article
- Development and validation of a machine learning-based prediction model for malnutrition risk in peritoneal dialysis patients: a multi‑center retrospective study.International urology and nephrology · 2026Article
- Development and temporal external validation of a high-specificity XGBoost rule-in model for diabetes in middle-aged and older Korean adults.Diabetology & metabolic syndrome · 2026Article
- What constitutes high risk for venous thromboembolism? Determining an appropriate threshold to initiate prophylaxis.Journal of thrombosis and haemostasis : JTH · 2026Article
- Association between HALP score and in-hospital outcomes in patients with acute kidney injury: a retrospective cohort study.Scientific reports · 2026Article
- Article
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Beyond AUC: Clinical interpretability of depression-MRI-machine learning analyses in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- The construction and validation of a nomogram for lung metastasis of intrahepatic cholangiocarcinoma: a study based on the SEER database.Translational cancer research · 2026Article
- Role of combined serum homocysteine and small dense LDL-C in predicting cardiovascular events in obesity: a retrospective clinical study.BMC cardiovascular disorders · 2026Article
- Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.Scientific reports · 2026Article
- Diagnosing Urinary Tract Infection in Young Febrile Children in the Emergency Department.JAMA network open · 2026Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A large number of prediction models are published with the objective of allowing personalized decision making for diagnostic or prognostic purposes. Conventional statistical measures of discrimination, calibration, or other measures of model performance are not well-suited for directly and clearly assessing the clinical value of scores or biomarkers. Decision curve analysis is an increasingly popular technique used to assess the clinical utility of a prognostic or diagnostic score/rule, or even of a biomarker. Clinical utility is expressed as the net benefit, which represents the net balance of patients' benefits and harms and considers, implicitly, the consequences of clinical actions taken in response to a certain prediction score, rule, or biomarker. The net benefit is plotted against a range of possible exchange rates, representing the spectrum of possible patients' and clinicians' preferences. Decision curve analysis is a powerful tool for judging whether newly published or existing scores may truly benefit patients, and represents a significant advancement in improving transparent clinical decision making. This paper is meant to be an introduction to decision curve analysis and its interpretation for clinical investigators. Given the extensive advantages, we advocate applying decision curve analysis to all models intended for use in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.