Development and Validation of an Algorithm for Item Reduction of the International Standards for Neurological Classification of Spinal Cord Injury Examination to Determine Level and Severity of SCI.
In 2020, a first, expedited version of the International Standards for Neurological Classification of Spinal Cord Injury (E-ISNCSCI-V1) was proposed for determination of neurological level of injury (NLI) and American Spinal Injury Association Impairment Scale (AIS) classifications. This work descri...
| Publicado en: | Topics in Spinal Cord Injury Rehabilitation Vol. 31; no. 3; pp. 61 - 68 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
KnowledgeWorks Global, Ltd
Summer2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187499935&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187499935 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10820744 0OF jtl: Topics in Spinal Cord Injury Rehabilitation issn: 10820744 maglogo: N pubinfo: dt: Summer2025 vid: 31 iid: 3 pid: 81084 pub: KnowledgeWorks Global, Ltd place: Richmond, Virginia artinfo: ui: 187499935 187499935 187499935 10.46292/sci25-00008 187499935 ppf: 61 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Validation of an Algorithm for Item Reduction of the International Standards for Neurological Classification of Spinal Cord Injury Examination to Determine Level and Severity of SCI. aug: au: Burns, Stephen P. Walden, Kristen Kirshblum, Steven Schmidt-Read, Mary Tansey, Keith Schuld, Christian Rupp, Ruediger affil: Spinal Cord Injury Service, VA Puget Sound Health Care System, Seattle, Washington sug: subj: Algorithms Instrument Construction Instrument Validation Spinal Cord Injuries Diagnosis Severity of Injury Evaluation Neurologic Examination Reproducibility of Results Clinical Assessment Tools Human Funding Source Male Female Classification Validation Studies Sensitivity and Specificity Motor Neurons Perception Computer Simulation Retrospective Design Multicenter Studies Protocols Diagnostic Errors Adolescence Adult Middle Age Aged Aged, 80 and Over Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: In 2020, a first, expedited version of the International Standards for Neurological Classification of Spinal Cord Injury (E-ISNCSCI-V1) was proposed for determination of neurological level of injury (NLI) and American Spinal Injury Association Impairment Scale (AIS) classifications. This work describes assessment of E-ISNCSCI-V1 classification accuracy and the development and data-based validation of an ISNCSCI Item Reduction Algorithm (IIRA). Classification accuracy for E-ISNCSCI-V1 examination shortcut options was assessed with automated analysis of 7026 full ISNCSCI examinations. Rules for the IIRA were iteratively adjusted to optimize the balance between omitting exam items and minimizing misclassification errors, and then it was validated through classification of 100 full ISNCSCI exams. If S1 findings are substituted for anorectal exam findings as proposed for E-ISNCSCI-V1, the error rate for AIS is 10%, with a high error rate (45%) for classifying true AIS B. The IIRA, which begins with full motor testing, followed by limited sensory testing required an average of 31% (42/134) of the full ISNCSCI exam items, with a 2% error rate for NLI and no AIS errors. The previously proposed E-ISNCSCI-V1, which included an option to substitute S1 findings for anorectal exam findings, is not recommended due to AIS error rate. The IIRA provides a standardized option for a shortened examination classifying NLI and AIS with high accuracy. It will serve as a basis for version 2 of the E-ISNCSCI. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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