Transplant of Serologic A1 but Genotype A2 Kidneys to ABO O and B Recipients is Feasible and Safe
Bibliographic record
Abstract
Background : Kidney transplants from ABO A2 donors to O and B recipients reduce wait times without compromising outcomes. Recent work shows that ABO genotyping more accurately identifies A2 donors than serologic methods. Here, we show that kidneys from donors labeled A1 by serology but genotyped as A2 can be safely transplanted to O and B recipients with favorable outcomes, expanding access for disadvantaged populations. Methods : All O and B patients who received ABO A 2 kidney transplants (N = 155, 2011–2025) were included in the study. All donors were genotyped as A2 or A2B and were stratified by serologic testing (anti-A 1 lectin) as A 1 or A 2 . Genotyping was performed by real time PCR or next generation sequencing. Data were analyzed for biopsy-proven rejection, anti-HLA/anti-ABO antibodies, and allograft loss. Results. 17 patients (11%) received kidneys from donors that were serologically identified as A 1 . In this cohort allograft survival was 100%. No graft losses were attributed to ABO incompatibility. Conclusions. Transplanting kidneys from donors labeled A 1 by serology but confirmed as A2 by genotyping into O and B recipients is safe and can expand the donor pool for disadvantaged candidates. Our findings support adopting ABO genotyping as a routine supplement to serologic testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".