Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

Journal: The Journal of manual & manipulative therapy
PMID: 39540649

Abstract

OBJECTIVES: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed 'Holy Grail' questions, one being: 'What are the most important (preventable) predictors of chronicity' for patients with LBP? Subsequently, prognostic factors influencing outcomes for CLBP have been described, however results remain conflicting due to methodological weaknesses.

Authors

  • Carolyn Cheema
    College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
  • Jonathan Baldwin
    College of Allied Health, Department of Medical Imaging and Radiation Sciences, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
  • Jason Rodeghero
    Department of Public Health & Community Medicine, School of Medicine, Tufts University, Boston, MA, USA.
  • Mark W Werneke
    Net Health Systems, Inc, Pittsburgh, PA, USA.
  • Jerry E Mioduski
    Net Health Systems, Inc, Pittsburgh, PA, USA.
  • Lynn Jeffries
    College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
  • Joseph Kucksdorf
    Bellin Health, Orthopedics and Sports Medicine, Green Bay, WI, USA.
  • Mark Shepherd
    Physical Therapy Department Bellin College, Green Bay, WI, USA.
  • Carol Dionne
    College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
  • Ken Randall
    College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, USA.

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