Blood Transfusion and Adverse Graft-related Events in Kidney Transplant Patients
Bibliographic record
Abstract
BACKGROUND: The impact of posttransplant red blood cell transfusion (RBCT) and their potential immunomodulatory effects on kidney transplant recipients are unclear. We examined the risks for adverse graft outcomes associated with post-kidney transplant RBCT. METHODS: We conducted a retrospective cohort study of all adult kidney transplant recipients at The Ottawa Hospital from 2002 to 2018. The exposure of interest was receipt of an RBCT after transplant categorized as 1, 2, 3 to 5, and >5 RBC. Outcomes of interest were rejection and death-censored graft loss (DCGL). Cox proportional hazards models were used to calculate hazard ratios (HR) with RBCT as a time-varying, cumulative exposure. RESULTS: Among 1258 kidney transplant recipients, 468 (37.2%) received 2373 total RBCTs, 197 (15.7%) had rejection and 114 (9.1%) DCGL. For the receipt of 1, 2, 3 to 5, and >5 RBCT, compared with individuals never transfused, the adjusted HRs (95% confidence interval [CI]) for rejection were 2.47 (1.62-3.77), 1.27 (0.77-2.11), 1.74 (1.00-3.05), and 2.23 (1.13-4.40), respectively; DCGL 2.32 (1.02-5.27), 3.03 (1.62-5.64), 7.50 (4.19-13.43), and 14.63 (8.32-25.72), respectively. Considering a time-lag for an RBCT to be considered an exposure before an outcome to limit reverse causation, RBCT was not associated with rejection; the HRs for DCGL attenuated but remained similar. RBCT was also associated with a negative control outcome, demonstrating possible unmeasured confounding. CONCLUSION: RBCT after kidney transplant is not associated with rejection, but may carry an increased risk for DCGL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".