Table 2.

ML concise overview of the chosen papers’ key characteristics and bias risk.

Author, Year	Country	Total Sample (TS)	Study Design	Intervention	Outcome	AIS Grade	NOS
DeVries 2019 [38]	Canada	862	Retrospective	The comparison of unsupervised MLA and LR, utilizing comprehensive neurological data for total admission, did not reveal any clinically significant disparities in functional prediction compared to previous models.	The F1-score has been demonstrated to possess greater reliability in evaluating algorithms than the area under the operating curve.	AIS A, B, C, and D	8
Torres 2021 [39]	USA	118	Retrospective	A similar network has been developed among patients to predict neurological recovery following spinal cord damage, focusing on MAP recorded before surgery.	The findings from the network analysis indicate that deviations from the optimal MAP range, either in the form of hypotension or hypertension, during surgical procedures are correlated with a reduced probability of achieving neurological recovery.	AIS A, B, C, D, and E	8
Agarwal 2022 [40]	USA	74	Retrospective	This study uses a deep-tree-based machine learning approach to evaluate the impact of intraoperative MAP and vasopressor administration on enhancing neurological outcomes in individuals with acute spinal cord injury.	An association between a MAP ranging from 80 to 96 mmHg and enhanced neurological function has been observed. Conversely, 93 min or more spent outside the MAP range of 76 to 104 mmHg had been associated with a worse outcome.	AIS A, B, C, D, and E	7

MLA: Machine Learning Algorithms; LR: Logistic Regression; MAP: Mean Arterial Pressure; NOS: Newcastle Ottawa Scale.