ReviewNature reviews. Rheumatology2019
Machine-learning-based patient-specific prediction models for knee osteoarthritis.
Review in Nature reviews. Rheumatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 4 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
109 citing papers in PubMed, 4 syntheses or guidelines pooled it, 189 citations in OpenAlex.
- Application of artificial intelligence in X-ray imaging analysis for knee arthroplasty: A systematic review.PloS one · 2025Pooled it
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Comparison of Warming Needle Moxibustion and Drug Therapy for Treating Knee Osteoarthritis: A Systematic Review and Meta-analysis.Computational and mathematical methods in medicine · 2022Pooled it
- Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight-Loss Treatments for Overweight and Obese Adults With Knee Osteoarthritis: Data From a Single-Center Randomized Trial.Arthritis care & research · 2021Trial
- The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes.American journal of hematology · 2021Trial
- Plasma proteomic signatures of early retinal neurodegeneration in diabetes: A multi-cohort study.PLoS medicine · 2026Observational
- Development of Machine Learning Algorithms Predicting Psychological Distress After Total Joint Arthroplasty.The Journal of arthroplasty · 2026Article
- Evolving Strategies for Knee Osteoarthritis: A Narrative Review of Integrated Rehabilitation, Pharmacologic, and Joint-Preserving Interventions.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now?Cureus · 2026Review
- Nomograms Based on X-Ray Radiomics for Predicting Pain Progression in Knee Osteoarthritis Using Data From the Foundation for the National Institutes of Health: Development and Validation Study.JMIR medical informatics · 2026Article
- Gender inequality in osteoarthritis: a bioinformatics perspective.Clinical rheumatology · 2026Article
- Exploration of acetylation-related biomarkers in osteoarthritis through bioinformatics analysis.PloS one · 2026Article
- Sympathetic regulation of bone homeostasis and remodeling: molecular mechanisms, pathophysiological roles, and therapeutic implications.Frontiers in immunology · 2026Review
- Development and validation of a generalisable survival model to predict osteoarthritis progression.Osteoarthritis and cartilage open · 2025Article
- MRI-based patient-specific nomogram for diagnostic risk stratification of patients with early knee OA.Rheumatology (Oxford, England) · 2025Article
- Knee osteoarthritis rehabilitation: an integrated framework of exercise, nutrition, biomechanics, and physical therapist guidance-a narrative review.European journal of medical research · 2025Review
- Photon-counting CT versus energy-integrating detector and flat-panel CT for cadaveric wrist arthrography with additional tin filter dose reduction.European radiology experimental · 2025Article
- Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 2025Article
49 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
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.
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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.