Screening for De Novo Anti-Human Leukocyte Antigen Antibodies in Nonsensitized Kidney Transplant Recipients Does Not Predict Acute Rejection
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine whether screening for anti-human leukocyte antigen (HLA) antibodies (Abs) could predict development of acute rejection (AR) before clinical evidence of kidney allograft dysfunction in nonsensitized recipients. METHODS: Eighty-four non-HLA identical kidney transplant recipients were prospectively tested for anti-HLA Abs (FlowPRA analysis and anti-HLA Ab specificity determination) at 0, 10, 20, 30, 60, 90, 180 and 365 posttransplantation, and at the time of clinical suspicion of AR. Allograft biopsies were performed at the time of engraftment, 3 and 12 months posttransplantation, when patients developed new anti-HLA Abs, or when clinically indicated. RESULTS: Among the 70 patients without preformed anti-HLA Abs, 11 developed de novo anti-HLA Abs (8 donor-specific Abs) at a median of 30 days (q1-q3=10-180 days) after transplantation. Patients with de novo anti-HLA Abs had a shorter time to AR than patients without de novo anti-HLA Abs, P=0.06. However, in all cases, de novo anti-HLA Abs developed concomitantly or after a clinically evident AR. CONCLUSIONS: Although de novo anti-HLA Abs were associated with AR, routine screening for anti-HLA Abs was not useful in identifying patients at risk for AR before clinical evidence of allograft dysfunction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".