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. 2023 Nov 2;18(11):e0294081. doi: 10.1371/journal.pone.0294081

Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome

Austin Chou, Abel Torres-Espin, Nikos Kyritsis, J Russell Huie, Sarah Khatry, Jeremy Funk, Jennifer Hay, Andrew Lofgreen, Rajiv Shah, Chandler McCann, Lisa U Pascual, Edilberto Amorim, Philip R Weinstein, Geoffrey T Manley, Sanjay S Dhall, Jonathan Z Pan, Jacqueline C Bresnahan, Michael S Beattie, William D Whetstone, Adam R Ferguson; the TRACK-SCI Investigators
PMCID: PMC10621810  PMID: 37917637

An additional affiliation is missing for author Adam R. Ferguson, who is also affiliated with San Francisco Veterans Affairs Healthcare System, San Francisco, California, United States of America.

The following information is missing from the Data Availability statement: Source data has been deposited to the Open Data Commons for Spinal Cord Injury (odc-sci.org;RRID:SCR_016673) under the accession number ODC-SCI:727 (http://doi.org/10.34945/F5KG6Z).

Reference

  • 1.Chou A, Torres-Espin A, Kyritsis N, Huie JR, Khatry S, Funk J, et al. (2022) Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. PloS ONE, 17(4): e0265254. 10.1371/journal.pone.0265254 [DOI] [PMC free article] [PubMed] [Google Scholar]

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