Selective Elimination and Rationalization of Cell-based Assays in Deceased Donor Kidney Transplant Crossmatching
Bibliographic record
Abstract
Background. While there is increasing reliance on a negative virtual crossmatch to proceed with deceased donor kidney transplantation, a flow cytometry crossmatch (FCXM) is still usually performed after the transplant has already occurred. Our center has eliminated pretransplant physical crossmatches for most patients, and since 2018, we have eliminated the systematic performance of posttransplant FCXMs. Methods. We studied all deceased donor kidney transplants in our program between June 1, 2018, and March 31, 2021, to evaluate the impact of eliminating retrospective FCXMs on resource utilization and graft outcomes (ie, the occurrence of antibody-mediated rejection [AMR] in the first 3-mo posttransplant). Results. A total of 358 kidney transplants occurred during the study period, and approximately 70% of these transplants proceeded without the performance of any FCXM. Incidence rates of AMR were low (9.63 per 1000 person-months), which compared favorably with the incidence rate of AMR during the 3-y period preceding the policy (4.82 per 1000 person-months, P = 0.21). Conclusions. Our results suggest that moving away from retrospective FCXM and relying exclusively on the virtual crossmatch is safe and efficient for kidney allocation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".