Evaluation of Transfusion Practices in Noncardiac Surgeries at High Risk for Red Blood Cell Transfusion: A Retrospective Cohort Study
Bibliographic record
Abstract
Perioperative bleeding is a major indication for red blood cell (RBC) transfusion, yet transfusion data in many major noncardiac surgeries are lacking and do not reflect recent blood conservation efforts. We aim to describe transfusion practices in noncardiac surgeries at high risk for RBC transfusion. We completed a retrospective cohort study to evaluate adult patients undergoing major noncardiac surgery at 5 Canadian hospitals between January 2014 and December 2016. We used Canadian Classification of Health Interventions procedure codes within the Discharge Abstract Database, which we linked to transfusion and laboratory databases. We studied all patients undergoing a major noncardiac surgery at ≥5% risk of perioperative RBC transfusion. For each surgery, we characterized the percentage of patients exposed to an RBC transfusion, the mean/median number of RBC units transfused, and platelet and plasma exposure. We identified 85 noncardiac surgeries with an RBC transfusion rate ≥5%, representing 25,607 patient admissions. The baseline RBC transfusion rate was 16%, ranging from 5% to 49% among individual surgeries. Of those transfused, the median (Q1, Q3) number of RBCs transfused was 2 U (1, 3 U); 39% received 1 U RBC, 36% received 2 U RBC, and 8% were transfused ≥5 U RBC. Platelet and plasma transfusions were overall low. In the era of blood conservation, we described transfusion practices in major noncardiac surgeries at high risk for RBC transfusion, which has implications for patient consent, preoperative surgical planning, and blood bank inventory management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".