Assessing the Necessity of Routine Crossmatching for Blood Transfusion in Renal Transplantation
Bibliographic record
Abstract
INTRODUCTION: Routine crossmatch of packed red blood cells (pRBCs) is completed preoperatively at many centers despite conflicting evidence on the incidence of blood transfusions with renal transplantation. In the current economic climate, resource adjudication should be judicious and medically appropriate. The objective of this study was to determine the incidence, timing, and predictors of early postoperative pRBC transfusion in patients undergoing renal transplantation. METHODS: A retrospective review of all patients undergoing renal transplantation at our institution from January 2013 to May 2016 was performed. Demographic, biochemical, and clinical parameters were recorded. The primary outcome was early postoperative transfusion, defined as an intraoperative transfusion or within 2 days of surgery. Multivariable logistic regression was performed to identify associations with early postoperative transfusion. RESULTS: We identified 428 patients during the study period (average age 55 years, 60% male, 30% obese, 67% deceased donor, and 43% preoperative antithrombotic use). Forty (9.3%) patients required early postoperative transfusion (mean: 2.8 pRBCs/transfusion) and most did not require blood urgently. Only 20 (4.7%) patients required a transfusion intraoperatively or on the same day of surgery. Lower preoperative hemoglobin (per g/L unit: odds ratio [OR]: 0.943), female gender (OR: 2.752), and preoperative antithrombotic use (OR 2.369) were associated with a need for early postoperative transfusion. CONCLUSION: Transfusion in the early postoperative period following renal transplantation was less than 10%, suggesting that routine crossmatch may not be necessary for all patients. Preoperative hemoglobin, female gender, and preoperative antithrombotic use were associated with increased risk and may be useful to risk-stratify patients who require crossmatch.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".