ReviewJournal of the American Medical Informatics Association : JAMIA2020
A tutorial on calibration measurements and calibration models for clinical prediction models.
Review in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 261 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
261 citing papers in PubMed, 2 syntheses or guidelines pooled it, 420 citations in OpenAlex.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Construction and evaluation of neonatal respiratory failure risk prediction model for neonatal respiratory distress syndrome.BMC pulmonary medicine · 2024Trial
- A Parsimonious Machine Learning Model for Predicting Invasive Coronary Angiography Referral After Coronary Computed Tomography Angiography: Development and Multicentre External Validation.Journal of clinical medicine · 2026Article
- Automating chart review for identification of infected patients using natural language processing.JAMIA open · 2026Article
- Calibrating Machine Learning Approaches for Probability Estimation Without Calibration Data.Statistics in medicine · 2026Article
- Rapid identification of feline sporotrichosis by ATR-FTIR spectroscopy coupled with machine learning.Analytical and bioanalytical chemistry · 2026Article
- Gut Microbial Topology and Metabolic Signatures Associated With Colorectal Neoplasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial Intelligence-Enabled Prescribing in Opioid Use Disorder Care: Decision Support, Risk Scores, Large Language Model Copilots, and Digital Therapeutics (2024-2025).European addiction research · 2026Review
- Multimodal large language models for bladder tumor detection in cystoscopy: a retrospective benchmarking study.World journal of urology · 2026Article
- Prediction of Atrial Fibrillation Occurrence With Handheld Mobile Electrocardiogram: Deep Learning Model Development Using Real-World Data.JMIR medical informatics · 2026Article
- A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.Scientific reports · 2026Article
- Abdominal fat thickness predicts the risk of pregnancy-associated hypertension: A prospective cohort study.Asia Pacific journal of clinical nutrition · 2026Article
- Prediction tools to prioritise hospitalised adult patients at risk of drug related problems: An umbrella review.PLOS digital health · 2026Article
- Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis.Health care science · 2026Review
- EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture.IEEE transactions on bio-medical engineering · 2026Article
- Detecting Adverse Events Following Acute Care Encounters among Veterans Prescribed Non-Steroidal Anti-Inflammatory Drugs.Journal of the American College of Emergency Physicians open · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Prediction of in-hospital cardiac arrest on general wards using calibrated machine learning.BMJ health & care informatics · 2026Article
- Time-Updated Prognostic Modeling in ICU Patients with Documented Coma or Unresponsiveness Using Routine Arterial Blood Gas Trajectories: An Exploratory Explainable Machine-Learning Study.Journal of clinical medicine · 2026Article
201 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 at 2 institutions in 1 country.
Funding
Abstract
Our primary objective is to provide the clinical informatics community with an introductory tutorial on calibration measurements and calibration models for predictive models using existing R packages and custom implemented code in R on real and simulated data. Clinical predictive model performance is commonly published based on discrimination measures, but use of models for individualized predictions requires adequate model calibration. This tutorial is intended for clinical researchers who want to evaluate predictive models in terms of their applicability to a particular population. It is also for informaticians and for software engineers who want to understand the role that calibration plays in the evaluation of a clinical predictive model, and to provide them with a solid starting point to consider incorporating calibration evaluation and calibration models in their work. Covered topics include (1) an introduction to the importance of calibration in the clinical setting, (2) an illustration of the distinct roles that discrimination and calibration play in the assessment of clinical predictive models, (3) a tutorial and demonstration of selected calibration measurements, (4) a tutorial and demonstration of selected calibration models, and (5) a brief discussion of limitations of these methods and practical suggestions on how to use them in 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.