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Comparison of the 2019 European Alliance of Associations for Rheumatology/American College of Rheumatology Systemic Lupus Erythematosus Classification Criteria With Two Sets of Earlier Systemic Lupus Erythematosus Classification Criteria.

Michelle A PetriDaniel W GoldmanGraciela S AlarconCaroline GordonJoan T MerrillPaul R FortinIan N BruceDavid Alan IsenbergDaniel WallaceOla NivedRosalind Ramsey-GoldmanSang-Cheol BaeJohn G HanlyJorge Sanchez-GuerreroAnn Elaine ClarkeCynthia AranowSusan ManziMurray B UrowitzDafna D GladmanKen KalunianVictoria P WerthAsad ZomaSasha BernatskyMunther KhamashtaSøren JacobsenJill P BuyonMary Anne DooleyRonald van VollenhovenEllen GinzlerThomas StollChristine PeschkenJoseph L JorizzoJeffery P CallenSung Sam LimMurat InançDiane L KamenAnisur RahmanKristjan SteinssonAndrew G FranksLaurence S Magder
Published in: Arthritis care & research (2021)
The 2 new weighted classification rules did not perform better than the existing list-based rules in terms of overall agreement on a data set originally generated to assess the SLICC criteria. Given the added complexity of summing weights, researchers may prefer the unweighted SLICC criteria. However, the performance of a classification rule will always depend on the populations from which the cases and non-cases are derived and whether the goal is to prioritize sensitivity or specificity.
Keyphrases
  • systemic lupus erythematosus
  • deep learning
  • machine learning
  • disease activity
  • magnetic resonance
  • juvenile idiopathic arthritis
  • artificial intelligence
  • big data
  • computed tomography
  • magnetic resonance imaging