A multidisciplinary comparison of transfusion and perioperative support for high‐risk cardiac surgery at three large academic centres in North America
Bibliographic record
Abstract
Cardiac surgery is associated with numerous peri- and post-operative haemostatic complications and blood transfusion requirements. Complex procedures such as redo-sternotomy heart transplantation or type A aortic dissection repairs are at high-risk for severe coagulopathy and significant transfusion requirements. However, current practice guidelines do not specifically address high-risk surgeries, resulting in variable practice. To optimise outcomes, a multidisciplinary approach to blood transfusion and haemostasis is critical. How individual institutions construct these multidisciplinary teams, delegate responsibilities, and build procedures may differ depending on the institution and availability of resources. In this article, we compare how the transfusion medicine services support their cardiac surgery and transplant programs at three large medical centres-Vanderbilt University Medical Center (the largest heart transplant centre in the world by volume in 2021), Toronto General Hospital-University Health Network (a quaternary-care centre in Canada's most populous city, performing more >20 heart transplants annually), and Vancouver General Hospital (a quaternary-care centre that performs numerous high-risk cardiac surgeries). This article discusses management from multiple perspectives, including the blood bank and perioperative environments, and highlights how institutions have evolved their programs in accordance with nation-specific policies and provisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".