Intraoperative Blood Transfusions: Identifying Stakeholder Interests
Bibliographic record
Abstract
Close to one million red blood cell (RBC) units are transfused annually in Canadian hospitals, with surgical inpatients accounting for up to 44% of transfusions. There is evidence of significant variation in transfusion practice in the operating room (i.e., intraoperative). Although variation is expected based on disease severity and patient preference, inappropriate clinical care due to either under- or over-transfusion likely also contributes to significant variation. Indeed, estimates of unwarranted intraoperative RBC transfusions in the literature range from 19% to 49%, owing partly to a lack of evidence-based consensus on RBC transfusion practice in the OR. Our two systematic reviews have highlighted this gap, demonstrating a lack of evidence from trials or actionable clinical practice guidelines to inform decisions in the OR. Perhaps more importantly, avoidance of blood product exposure is an important patient-prioritized outcome that has yet to be studied empirically in the OR. As such, the observed variation in transfusion practice suggests that transfusion decision-making during surgery represents a clear and important knowledge and evidence gap. Transfusion decision-making in the OR is a complex and dynamic process that we cannot begin to improve without first understanding it. It is influenced by 1) physiologic parameters such as acute blood loss, the effects of general anesthesia, and surgical manipulation. Decision-making is also likely heavily influenced by 2) behavioural factors in the OR (heuristics, team dynamics, institutional culture), for which very little empirical work has been conducted. Finally, the importance of 3) patient input in influencing transfusion decisions is inadequately studied, given the documented disconnect between patient priorities and outcomes used in the medical literature and by clinicians. In this context, the aim of my thesis was to develop an empirical understanding of transfusion decision-making in the OR based on stakeholder perceptions and priorities, informed by an integrated patient engagement process. With this work, I address an important knowledge gap in intraoperative blood transfusion, thereby contributing to efforts to reduce variation in blood transfusion practice in surgery. It is my hope that this work will be influential in informing actionable perioperative tools to optimize blood management including providing both evidence and knowledge gaps for future research.
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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 | 0.068 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".