Pretransplant HLA Antibodies Are Associated with Reduced Graft Survival After Clinical Islet Transplantation
Bibliographic record
Abstract
Despite significant improvements in islet transplantation, long-term graft function is still not optimal. It is likely that both immune and nonimmune factors are involved in the deterioration of islet function over time. Historically, the pretransplant T-cell crossmatch and antibody screening were done by anti-human globulin--complement-dependent cytotoxicity (AHG-CDC). Class II antibodies were not evaluated. In 2003, we introduced solid-phase antibody screening using flow-based beads and flow crossmatching. We were interested to know whether pretransplant human leukocyte antigen (HLA) antibodies or a positive flow crossmatch impacted islet function post-transplant. A total of 152 islet transplants was performed in 81 patients. Islet function was determined by a positive C-peptide. Results were analyzed by procedure. Class I and class II panel reactive antibody (PRA) > 15% and donor-specific antibodies (DSA) were associated with a reduced C-peptide survival (p<0.0001 and p<0.0001, respectively). A positive T- and or B-cell crossmatch alone was not. Pretransplant HLA antibodies detectable by flow beads are associated with reduced graft survival. This suggests that the sirolimus and low-dose tacrolimus-based immunosuppression may not control the alloimmune response in this presensitized population and individuals with a PRA > 15% may require more aggressive inductive and maintenance immunosuppression, or represent a group that may not benefit from islet transplantation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".