Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion.

51 , 52 This approach risks discarding data with borderline significance, whereas machine‐learning models rely on the additive effects of both strong and weak predictors, 29 which may additionally explain why these models perform better.

also in 7,017 other papers

The T2W:L2, which was highly significant in univariable analysis, emerged as the most influential variable in the XGBoost model's decision making.

also in 132,142 other papers