Early Posttransplant Blood Transfusion and Risk for Worse Graft Outcomes
Bibliographic record
Abstract
Introduction Blood transfusion is a risk factor for allosensitization. Nevertheless, blood transfusion posttransplant remains a common practice. We evaluated the effect of posttransplant blood transfusion on graft outcomes. Methods We included nonsensitized, first-time, kidney-alone recipients transplanted between 1 July 2015 and 31 December 2017. Patients were grouped based on receiving blood transfusion in the first 30 days posttransplant. The primary end point was a composite outcome of biopsy-proven acute rejection, death of any cause, or graft failure in the first year posttransplant. Secondary outcomes included the individual components of the primary outcome and the cumulative incidence of de novo donor-specific antibodies (DSAs). Results Two hundred seventy-three patients were included. One hundred twenty-seven (47%) received blood transfusion. Patients in the transfusion group were more likely to be older, have had a deceased donor, and have received induction with basiliximab. There was no difference between groups in the composite primary outcome (adjusted hazard ratio = [HR] 1.34; 95% confidence interval [CI], 0.83–2.17; P = 0.23). The cumulative incidence of de novo DSAs during the first year posttransplant was similar between groups (12.8% transfusion vs. 10.9% no transfusion, P = 0.48). Conclusion Early transfusion of blood products in kidney transplant recipients receiving induction with lymphocyte depletion was not associated with an increased hazard of experiencing acute rejection, death from any cause, or graft loss.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".