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dc.creatorVíctor Manuel Treviño Alvarado
dc.creatorJosé Gerardo Tamez Peña
dc.date2015
dc.date.accessioned2018-10-18T20:35:00Z
dc.date.available2018-10-18T20:35:00Z
dc.identifier.issn1748670X
dc.identifier.doi10.1155/2015/794141
dc.identifier.urihttp://hdl.handle.net/11285/630415
dc.descriptionIn this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. © 2015 Jorge I. Galván-Tejada et al.
dc.languageeng
dc.publisherHindawi Limited
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84945377076&doi=10.1155%2f2015%2f794141&partnerID=40&md5=e2e224d70edeb2d5f859afaf88a02c96
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceComputational and Mathematical Methods in Medicine
dc.subjectadult
dc.subjectArticle
dc.subjectcomputer assisted diagnosis
dc.subjectcomputer prediction
dc.subjectcontrolled study
dc.subjectdata analysis
dc.subjectdiagnostic accuracy
dc.subjectelastic tissue
dc.subjectfemale
dc.subjectfemur condyle
dc.subjecthuman
dc.subjectincidence
dc.subjectinformation processing
dc.subjectknee pain
dc.subjectmale
dc.subjectmiddle aged
dc.subjectnormal human
dc.subjectosteophyte
dc.subjectpain assessment
dc.subjectphysician
dc.subjectquantitative analysis
dc.subjectquantitative study
dc.subjectradiography
dc.subjectaged
dc.subjectbiological model
dc.subjectcase control study
dc.subjectcomputer simulation
dc.subjectdiagnostic imaging
dc.subjectfactual database
dc.subjectimage enhancement
dc.subjectknee
dc.subjectknee osteoarthritis
dc.subjectlongitudinal study
dc.subjectmultivariate analysis
dc.subjectpain
dc.subjectpain measurement
dc.subjectpathophysiology
dc.subjectstatistical model
dc.subjectstatistics and numerical data
dc.subjectAged
dc.subjectCase-Control Studies
dc.subjectComputer Simulation
dc.subjectDatabases, Factual
dc.subjectFemale
dc.subjectHumans
dc.subjectKnee Joint
dc.subjectLinear Models
dc.subjectLongitudinal Studies
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectModels, Biological
dc.subjectMultivariate Analysis
dc.subjectOsteoarthritis, Knee
dc.subjectPain
dc.subjectPain Measurement
dc.subjectRadiographic Image Enhancement
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleMultivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI
dc.typeArtículo
dc.identifier.volume2015
refterms.dateFOA2018-10-18T20:35:00Z


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