ArticleOsteoarthritis and cartilage2019
A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.
Article in Osteoarthritis and cartilage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Pooled it
- Risk factors for pain and functional impairment in people with knee and hip osteoarthritis: a systematic review and meta-analysis.BMJ open · 2020Pooled it
- Archetypal analysis of pain measures in a diverse population-based cohort: Exploring cross-sectional relationships with outcomes.Osteoarthritis and cartilage open · 2026Article
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- The osteoarthritis inflammatory ecosystem: a node-edge-boundary-emergent framework toward precision stratification and therapeutic hypothesis generation.Frontiers in medicine · 2026Review
- Development and validation of a generalisable survival model to predict osteoarthritis progression.Osteoarthritis and cartilage open · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Magnetic Resonance Imaging Biomarkers of Knee Osteoarthritis Progression.ACR open rheumatology · 2025Article
- Knee Osteoarthritis Diagnosis: Future and Perspectives.Biomedicines · 2025Review
- Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Beyond symptomatic alignment: evaluating the integration of causal mechanisms in matching animal models with human pathotypes in osteoarthritis research.Arthritis research & therapy · 2025Review
- Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.International journal of molecular sciences · 2025Review
- The causal role of brain circuits in osteoarthritis pain.Nature reviews. Rheumatology · 2025Review
- Severity and progression of structural hand OA is not associated with progression of structural knee OA: The IMI-APPROACH cohort.Osteoarthritis and cartilage open · 2024Article
- Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data.Scientific reports · 2024Article
- Performance of Radiological and Biochemical Biomarkers in Predicting Radio-Symptomatic Knee Osteoarthritis Progression.Biomedicines · 2024Article
- Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis.Journal of pharmacy & bioallied sciences · 2024Article
- Machine learning and deep neural network-based learning in osteoarthritis knee.World journal of methodology · 2023Review
- Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Sex-specific effects of calving season on joint health and biomarkers in Montana ranchers.BMC musculoskeletal disorders · 2023Article
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8 authors at 1 institution in 1 country.
Funding
Abstract
objectiveKnee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.
designWe used publicly available data from the Foundation for the National Institutes of Health (FNIH) osteoarthritis (OA) Biomarkers Consortium, where radiographic (medial joint space narrowing of ≥0.7 mm), and pain progression (increase of ≥9 Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] points) were defined at 48 months, as four mutually exclusive outcome groups (none, both, pain only, radiographic only), along with an extensive set of covariates. We applied distance weighted discrimination (DWD), direction-projection-permutation (DiProPerm) testing, and clustering methods to focus on the contrast (z-scores) between those progressing by both criteria ("progressors") and those progressing by neither ("non-progressors").
resultsUsing all observations (597 individuals, 59% women, mean age 62 years and BMI 31 kg/m
conclusionsUsing methods that provide a way to assess numerous variables of different types and scalings simultaneously in relation to an outcome of interest enabled a data-driven approach that identified key variables associated with a progression phenotype.
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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.