Blood transfusion and the risk for infections in kidney transplant patients
Bibliographic record
Abstract
BACKGROUND: Receipt of a red blood cell transfusion (RBCT) post-kidney transplantation may alter immunity which could predispose to subsequent infection. METHODS: We carried out a single-center, retrospective cohort study of 1,258 adult kidney transplant recipients from 2002 to 2018 (mean age 52, 64% male). The receipt of RBCT post-transplant (468 participants transfused, total 2,373 RBCT) was analyzed as a time-varying, cumulative exposure. Adjusted cox proportional hazards models were used to calculate hazard ratios (HR) for outcomes of bacterial or viral (BK or CMV) infection. RESULTS: Over a median follow-up of 3.8 years, bacterial infection occurred in 34% of participants at a median of 409 days post-transplant and viral infection occurred in 25% at a median of 154 days post-transplant. Transfusion was associated with a step-wise higher risk of bacterial infection (HR 1.35, 95%CI 0.95-1.91; HR 1.29, 95%CI 0.92-1.82; HR 2.63, 95%CI 1.94-3.56; HR 3.38, 95%CI 2.30-4.95, for 1, 2, 3-5 and >5 RBCT respectively), but not viral infection. These findings were consistent in multiple additional analyses, including accounting for reverse causality. CONCLUSION: Blood transfusion after kidney transplant is associated with a higher risk for bacterial infection, emphasizing the need to use transfusions judiciously in this population already at risk for infections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".