Current practices in perioperative blood management for patients undergoing liver resection: a survey of surgeons and anesthesiologists
Bibliographic record
Abstract
BACKGROUND: Development of intraoperative techniques and blood management strategies in liver resection, and the multidisciplinary nature of perioperative transfusion decision making, creates an opportunity for practice variation. The aim of this study was to describe the current practices in perioperative blood management and explore differences between surgeons and anesthesiologists. STUDY DESIGN AND METHODS: A Web-based survey was developed, piloted, and circulated to Canadian liver surgeons and anesthesiologists. The survey focused on management of preoperative anemia, blood conservation strategies, estimation of blood loss, and transfusion decision making in a multidisciplinary setting. RESULTS: A total of 198 physicians received the survey, with 117 responding (59%). Most responding surgeons (67%) perform more than 20 liver resections per year, while most responding anesthesiologists (90%) take part in fewer than 20. Anesthesiologists most commonly stated that preoperative anemia is managed by someone else (38%), while surgeons most commonly reported "no specific treatment" (45%). The most common intraoperative blood conservation technique used is administration of antifibrinolytics (63% used them at least occasionally). The most important factor for anesthesiologists when deciding on an intraoperative transfusion was hemoglobin value (47%); for surgeons, it was patient hemodynamics (33%). Compared to when they started their career, 60% of respondents felt that they were less likely to transfuse a patient now. CONCLUSION: The results of our survey provide insights into current transfusion practice and decision making in liver resection, including a comparison between anesthesiologist and surgeon transfusion behavior. Management of preoperative anemia, increased use of intraoperative blood conservation techniques, and improved communication between providers were identified as targets for quality improvement.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".