<p>Developing a machine learning model to predict fresh frozen plasma requirements during Stanford type A acute aortic dissection surgery using early ROTEM FIBTEM Data</p>

Tomoko Ishibashi; Kota Ishibashi; Takeshi Fukatsu

doi:10.7490/f1000research.1120404.1

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Ishibashi T, Ishibashi K and Fukatsu T. Developing a machine learning model to predict fresh frozen plasma requirements during Stanford type A acute aortic dissection surgery using early ROTEM FIBTEM Data [version 1; not peer reviewed]. F1000Research 2025, 14:1310 (poster) (https://doi.org/10.7490/f1000research.1120404.1)

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Developing a machine learning model to predict fresh frozen plasma requirements during Stanford type A acute aortic dissection surgery using early ROTEM FIBTEM Data

Tomoko Ishibashi¹, Kota Ishibashi, Takeshi Fukatsu

Published 26 Nov 2025 (https://doi.org/10.7490/f1000research.1120404.1)

Author Affiliations

¹ Department of Anesthesia, Tokyo Bay Urayasu Ichikawa Medical Center, Urayasu-shi, Chiba, Japan

Copyright © 2025 Ishibashi T et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 26 Nov 2025

Developing a machine learning model to predict fresh frozen plasma requirements during Stanford type A acute aortic dissection surgery using early ROTEM FIBTEM Data
[version 1; not peer reviewed]

Tomoko Ishibashi¹, Kota Ishibashi, Takeshi Fukatsu

Author Affiliations

¹ Department of Anesthesia, Tokyo Bay Urayasu Ichikawa Medical Center, Urayasu-shi, Chiba, Japan

Abstract

Competing Interests

No competing interests were disclosed

Keywords

ROTEM, FIBTEM, Fresh Frozen Plasma, FFP

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