International Journal of Surgery Publish Ahead of Print

DOI:10.1097/JS9.0000000000003457

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Letter  to  the  Editor:Predicting  intraoperative  blood  loss  risk  in  severe lumbar  disc  herniation  patients  undergoing  PLIF:a  multicenter  cohort study using ensemble learning

Shiquan Pan1,Long jun Chen2

1 Yulin Hospital of Integrated Traditional Chinese and Western Medicine Orthopedics,Department of Orthopedics.Yulin,china

2 Ningbo Yinzhou Zhedong Orthopaedic Hospital,Ningbo,315000,China

Correspondence Email:

Long jun Chen: 19538852911@163.com

Letter     to     the     Editor:Predicting     intraoperative     blood     loss      risk     in     severe

lumbar  disc  herniation  patients  undergoing  PLIF:a  multicenter  cohort study using ensemble learning

We read with interest the recently published article on the development ofthe IBLED-LDH model,an ensemble machine learning framework designed to predict intraoperative blood loss (IBL)in patients with severe lumbar disc herniation (LDH)undergoing posterior lumbar interbody fusion(PLIF)(1). While the authors should be commended for leveraging large-scale,multicenter data and modern ML techniques to address personalized surgical risk stratification,several methodological and conceptual concerns merit further discussion.

The definition of "excessive IBL"in this study is based on a composite outcome:(1)intraoperative blood  loss  exceeding  the  75th  percentile(>400  mL),(2)a  hemoglobin  drop  of≥10  g/dL,or(3) intraoperative blood transfusion.However,these thresholds appear arbitrary and highly context-

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