ArticleEuropean journal of translational myology2022
The role of bone mineral density and cartilage volume to predict knee cartilage degeneration.
Article in European journal of translational myology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Adaptive Force of hamstring muscles is reduced in patients with knee osteoarthritis compared to asymptomatic controls.BMC musculoskeletal disorders · 2024Article
- Association between serum soluble α-klotho and bone mineral density (BMD) in middle-aged and older adults in the United States: a population-based cross-sectional study.Aging clinical and experimental research · 2023Article
- Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Excessive Sagittal Slope of the Tibia Component during Kinematic Alignment-Safety and Functionality at a Minimum 2-Year Follow-Up.Journal of personalized medicine · 2022Article
- An Experimental and Virtual Approach to Hip Revision Prostheses.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.
Identifiers
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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.