Flow Cytometric Crossmatching in Primary Renal Transplant Recipients with a Negative Anti-Human Globulin Enhanced Cytotoxicity Crossmatch
Bibliographic record
Abstract
Flow cytometric crossmatching (FCXM) and panel reactive antibody (PRA) screening techniques are more sensitive than anti-human globulin enhanced cytotoxicity (AHG-CDC) techniques at detecting anti-HLA antibodies. The clinical significance of a positive FCXM in primary renal transplant recipients with a negative AHG-CDC crossmatch is unclear. We performed retrospective FCXM and flow cytometric panel reactive antibody (FlowPRA) determinations in primary renal transplant recipients with a negative T cell AHG-CDC crossmatch and a negative B cell CDC crossmatch pretransplant. Eighteen (13%) of 143 patients exhibited a positive retrospective T cell FCXM. Of these patients, six (33%) experienced early graft loss with explant histology, demonstrating antibody-mediated rejection in five of six cases. The 12 patients with positive T cell FCXM who maintained their grafts experienced more adverse events posttransplant, including more early, steroid-resistant, and recurrent rejection. Furthermore, in a subgroup of patients undergoing protocol biopsies, those with a positive T cell FCXM exhibited more subclinical rejection. Anti-HLA antibodies were detected by FlowPRA in all 18 patients with a positive T cell FCXM, whereas AHG-CDC PRA detected antibodies in only 8 of 18 patients. Therefore, flow cytometric techniques identify sensitized primary renal transplant recipients undetected by AHG-CDC techniques. In those patients, a positive T cell FCXM is associated with an increased risk of early graft loss due to antibody-mediated rejection and may represent a relative contraindication to transplantation. Moreover, those patients are also at increased risk of severe and recurrent rejection, which may carry implications for long-term graft outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".