[This corrects the article DOI: 10.1021/acsomega.2c06615.].
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http://dx.doi.org/10.1021/acsomega.4c08155 | DOI Listing |
Behav Sci (Basel)
December 2024
Institute of Education, Xiamen University, Xiamen 361005, China.
The authors would like to adjust the author order for the following two reasons: 1 [...
View Article and Find Full Text PDFAnn Surg
January 2025
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Objective: To assess performance of an algorithm for automated grading of surgery-related adverse events (AEs) according to Clavien-Dindo (C-D) classification.
Summary Background Data: Surgery-related AEs are common, lead to increased morbidity for patients, and raise healthcare costs. Resource-intensive manual chart review is still standard and to our knowledge algorithms using electronic health record (EHR) data to grade AEs according to C-D classification have not been explored.
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