Frequency and Clinical Implications of Development of Donor-Specific and Non–Donor-Specific HLA Antibodies after Kidney Transplantation
Bibliographic record
Abstract
The involvement of immunologic and nonimmunologic events in long-term kidney allograft failure is difficult to assess. The development of HLA antibodies after transplantation is the witness of ongoing reactivity against the transplant, and several studies have suggested that the presence of HLA antibodies correlates with poor graft survival. However, they have not discriminated between donor-specific (DS) and non-specific (NDS) antibodies. A total of 1229 recipients of a kidney graft, transplanted between 1972 and 2002, who had over a 5-yr period a prospective annual screening for HLA antibodies with a combination of ELISA, complement-dependent cytotoxicity, and flow cytometry tests were investigated; in 543 of them, the screening was complete from transplantation to the fifth year postgrafting. Correlations were established between the presence and the specificity of the antibodies and clinical parameters. A total of 5.5% of the patients had DS, 11.3% had NDS, and 83% had no HLA antibodies after transplantation. NDS antibodies appeared earlier (1 to 5 yr posttransplantation) than DS antibodies (5 to 10 yr). In multivariate analysis, HLA-DR matching, pretransplantation immunization, and acute rejection were significantly associated with the development of both DS and NDS antibodies and also of DS versus NDS antibodies. The presence of either DS or NDS antibodies significantly correlated with lower graft survival, poor transplant function, and proteinuria. Screening of HLA antibodies posttransplantation could be a good tool for the follow-up of patients who receive a kidney transplant and allow immunosuppression to be tailored.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".