ArticleMedical decision making : an international journal of the Society for Medical Decision Making2019
Assessing the Clinical Impact of Risk Models for Opting Out of Treatment.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Bacteremia prediction models to reduce unnecessary blood cultures: external validation in a large US emergency department.Scientific reports · 2026Article
- An Integrated Multi-Omics Analysis Identifies Oxeiptosis-Related Biomarkers in Diabetic Retinopathy.Biomedicines · 2025Article
- Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Biomarkers for the diagnosis of indeterminate pulmonary nodules: are we there yet?Journal of thoracic disease · 2025Review
- Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Recalibration Methods for Improved Clinical Utility of Risk Scores.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Development and Validation of the Acute PNeumonia Early Assessment Score for Safely Discharging Low-Risk SARS-CoV-2-Infected Patients from the Emergency Department.Journal of clinical medicine · 2022Article
- Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis.International journal of general medicine · 2022Article
- Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia.Diagnostic and prognostic research · 2021Article
- Metrics for Evaluating Polygenic Risk Scores.JNCI cancer spectrum · 2021Article
- A predictive nomogram for mortality of cancer patients with invasive candidiasis: a 10-year study in a cancer center of North China.BMC infectious diseases · 2021Observational
- Noninvasive biomarkers for lung cancer diagnosis, where do we stand?Journal of thoracic disease · 2020Review
- Predicting Residual Function in Hemodialysis and Hemodiafiltration-A Population Kinetic, Decision Analytic Approach.Journal of clinical medicine · 2019Article
- The Importance of Uncertainty and Opt-In v. Opt-Out: Best Practices for Decision Curve Analysis.Medical decision making : an international journal of the Society for Medical Decision Making · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Decision curves are a tool for evaluating the population impact of using a risk model for deciding whether to undergo some intervention, which might be a treatment to help prevent an unwanted clinical event or invasive diagnostic testing such as biopsy. The common formulation of decision curves is based on an opt-in framework. That is, a risk model is evaluated based on the population impact of using the model to opt high-risk patients into treatment in a setting where the standard of care is not to treat. Opt-in decision curves display the population net benefit of the risk model in comparison to the reference policy of treating no patients. In some contexts, however, the standard of care in the absence of a risk model is to treat everyone, and the potential use of the risk model would be to opt low-risk patients out of treatment. Although opt-out settings were discussed in the original decision curve paper, opt-out decision curves are underused. We review the formulation of opt-out decision curves and discuss their advantages for interpretation and inference when treat-all is the standard.
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.