Max E R Marsden

基本信息

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职业迁徙

个人简介

I am an Honorary Clinical Lecturer in the Centre for Trauma Science at the Blizard Institute, Queen Mary, University of London. My research interests are in Trauma, specifically in decision making pre-hospital, bleeding and transfusion. I am currently working on Decision Support with Bayesian Network models and endovascular haemorrhage control and resuscitation.

研究兴趣

论文共 60 篇作者统计合作学者相似作者

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Enhancing the Clinical Relevance of Haemorrhage Prediction Models in Trauma

Sankalp Tandle,Jared M.Wohlgemut,Max E.R.Marsden,Erhan Pisirir,Evangelia Kyrimi,Rebecca S.Stoner,William Marsh,Zane B.Perkins,Nigel R.M.Tai

Military Medical Research（2024）

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Introduction of Penthrox to the defence medical services: early findings

Stuart McPhail, A Thornback, D Ochiltree,M E R Marsden, A Kumar

BMJ military health (2024)

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Prehospital Partial Resuscitative Endovascular Balloon Occlusion of the Aorta for Exsanguinating Subdiaphragmatic Hemorrhage

Robbie A Lendrum,Zane Perkins,Max Marsden,Claire Cochran,Ross Davenport, Frank Chege, Virginia Fitzpatrick-Swallow,Rob Greenhalgh,Jared M Wohlgemut, Christine L Henry,Ben Singer,Gareth Grier,Gareth Davies, Nick Bunker,Daniel Nevin, Mike Christian,Marion K Campbell,Nigel Tai, Austin Johnson,Jan O Jansen,Samy Sadek,Karim Brohi

JAMA surgeryno. 9 (2024): 998-1007

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ASGBI Trauma Committee Position Statement: a National Audit of Trauma Laparotomy in the UK - an Unmet Need

Joanna M. Shepherd,Paul Vulliamy,Max E. R. Marsden,Kate Hancorn

BRITISH JOURNAL OF ANAESTHESIAno. 5 (2024): 849-850

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Occult tension pneumothorax discovered following imaging for adult trauma patients in the modern major trauma system: a multicentre observational study

David N. Naumann,E. Sellon, S. Mitchinson, H. Tucker,M. E. R. Marsden, E. Norris-Cervetto, V Bafitis, T. Smith,R. Bradley, A. Alzarrad, S. Naeem, G. Smith, S. Dillane, A. Humphrys-Eveleigh,M. Wordsworth, N. Sanchez-Thompson, D. Bootland, L. Brown

BMJ MILITARY HEALTHno. 2 (2024): 123-129

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Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis