Utilizing a surgical sabermetrics framework during major haemorrhage simulation to assess trainee cognitive load and performance
Abstract Vascular surgery training programs lack high-fidelity simulation for management of major haemorrhage, despite it being a fundamental and demanding skill. Cognitive load (CogL) is the finite working memory resource required to meet the demands of a task. Cognitive overload occurs when demand exceeds capacity, and this can negatively affect both technical and non-technical skills in surgery. Surgical Sabermetrics is an evolving field focusing on the advanced analytics of data derived from procedural audiovisual recordings and non-invasive physiological sensors measuring surgeon CogL. The planned simulation will assess surgical trainee management of major haemorrhage from a femoral artery model in a high-fidelity simulated operating room. It will immediately follow on from a separate but related simulation in which trainees are subjected to either critical or supportive trainer behavior during a routine femoral anastomosis. The study aims to assess whether prior exposure to incivility impacts trainee performance or cognition when independently managing a high pressure major bleeding scenario. A sabermetrics framework will be utilized to collate objective data about participant technical performance, non-technical skills and CogL. Full ethical approval has been obtained for this study. Dissemination of the results will be through conferences and publications in peer reviewed journals.
Authors
- Andrew L. Tambyraja (ORCID: https://orcid.org/0000-0002-9393-6291)
- Steven Yule (ORCID: https://orcid.org/0000-0001-9889-9090)
- Connor Boyle (ORCID: https://orcid.org/0000-0003-2124-607X)
- Joel Norton (ORCID: https://orcid.org/0000-0003-3197-4628)
- Lachlan Dick (ORCID: https://orcid.org/0000-0002-4341-9501)
- Elgan-Manton Roseblade
Institutions
- Edinburgh Royal Infirmary (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of Surgical Protocols and Research Methodologies
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/jsprm/snaf016
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00