Assessing and validating machine learning–enhanced imputation of admission American Spinal Injury Association Impairment Scale grades for spinal cord injury
A data-driven approach to imputation resulted in a robust method for imputing admission AIS grades that demonstrated clinical validity in the SCIMS database. This approach extends the utility of this longitudinal database and may provide a framework for other SCI databases.
Authors
- Daniel Lubelski (ORCID: https://orcid.org/0000-0002-9403-9509)
- Kelly Jiang (ORCID: https://orcid.org/0000-0002-1144-7882)
- Kathleen R. Ran (ORCID: https://orcid.org/0000-0001-5573-7534)
- Timothy F. Witham (ORCID: https://orcid.org/0000-0002-9397-8344)
- Andrew M. Hersh (ORCID: https://orcid.org/0000-0003-1755-3974)
- A. Daniel Davidar (ORCID: https://orcid.org/0000-0002-1508-3449)
- Carlos A. Aude (ORCID: https://orcid.org/0000-0002-1253-1998)
- Tej D. Azad (ORCID: https://orcid.org/0000-0001-7823-4294)
- Nicholas Theodore (ORCID: https://orcid.org/0000-0001-5355-2683)
- Jacob Jo (ORCID: https://orcid.org/0000-0002-0424-3507)
- Carly Weber-Levine (ORCID: https://orcid.org/0000-0003-2057-8419)
- Ali Bydon (ORCID: https://orcid.org/0000-0002-3058-1470)
- Ritvik Jillala (ORCID: https://orcid.org/0000-0003-3061-7338)
- Vikas N. Vattipally (ORCID: https://orcid.org/0009-0000-6920-2311)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- Journal of Neurosurgery Spine
- Published
- 2025-05-09
- DOI
- https://doi.org/10.3171/2025.1.spine241135
- Citations
- 1
- Primary Topic
- Spinal Cord Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.47