Comment on "an interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study".

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Title: Comment on "an interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study".
Authors: Mayasala P; Department of Information Technology, Malla Reddy University, Hyderabad, Telangana, 500100, India., Kumar GS; Department of Information Technology, Malla Reddy University, Hyderabad, Telangana, 500100, India., Neelu L; Department of Information Technology, Malla Reddy University, Hyderabad, Telangana, 500100, India. drlalband.neelu@mallareddyuniversity.ac.in.
Source: Journal of anesthesia, analgesia and critical care [J Anesth Analg Crit Care] 2026 Jun 30; Vol. 6 (1). Date of Electronic Publication: 2026 Jun 30.
Publication Type: Letter
Journal Info: Publisher: Springer Nature, BioMed Central Ltd Country of Publication: England NLM ID: 9918591885906676 Publication Model: Electronic Cited Medium: Internet ISSN: 2731-3786 (Electronic) Linking ISSN: 27313786 NLM ISO Abbreviation: J Anesth Analg Crit Care Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2731-3786
DOI:10.1186/s44158-026-00380-0