Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5677706 | Archives of Physical Medicine and Rehabilitation | 2016 | 33 Pages |
Abstract
After further prospective validation, such predictive models may allow clinicians to use data available at the time of admission to inpatient spinal cord injury rehabilitation to accurately predict longer-term ambulation status, and whether individual patients are likely to perform various self-care activities with or without assistance from another person.
Keywords
SCIMSANNAISPLRNLRAUCAsiaSpinal cord injuriesSpinal cord injuryAmerican Spinal Injury AssociationRehabilitationDecision support techniquesSpinal Cord Injury Model SystemsArtificial Neural NetworksciActivities of daily livingAmerican Spinal Injury Association Impairment Scalearea under the curvepositive likelihood rationegative likelihood ratioMedical informatics
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Authors
Timothy PhD, Alan M. PT, PhD, Subramani MD, Jeffrey MBA, David MD, Daniel PhD, Bethlyn MSW, MPH, Mary PT, PhD, Chantal PhD,