ArticleSurgery2023
Evaluating prediction model performance.
Article in Surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Article
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Optimized image preprocessing strategies for enhanced neural network-based defect detection in industrial automation.Scientific reports · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data.JMIR formative research · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- Artificial Intelligence-Enabled Electrocardiography for Prediction of Sudden Cardiac Death and Malignant Ventricular Arrhythmias: A Scoping Review.Journal of cardiovascular development and disease · 2026Review
- Machine learning-based prediction of subclinical mastitis in large-scale dairy herds using a locally established somatic cell count threshold.Scientific reports · 2026Article
- Identification of Key Osteoarthritis-Associated Genes Based on DNA Methylation.International journal of molecular sciences · 2026Article
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- Application of machine learning in the research progress of post-kidney transplant rejection.World journal of transplantation · 2026Review
- Comment on "Development and validation of a minimally invasive diagnostic model for biliary atresia using artificial intelligence".World journal of pediatrics : WJP · 2026Article
- Evaluating the Diagnostic Accuracy of Ultrasound and Clinical Predictors in Pediatric Acute Tip Appendicitis:Saudi medical journal · 2026Article
- Article
- Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.Asia Pacific journal of clinical nutrition · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This article highlights important performance metrics to consider when evaluating models developed for supervised classification or regression tasks using clinical data. When evaluating model performance, we detail the basics of confusion matrices, receiver operating characteristic curves, F1 scores, precision-recall curves, mean squared error, and other considerations. In this era, defined by the rapid proliferation of advanced prediction models, familiarity with various performance metrics beyond the area under the receiver operating characteristic curves and the nuances of evaluating model value upon implementation is essential to ensure effective resource allocation and optimal patient care delivery.
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.