14th International HLA and Immunogenetics Workshop: Report on the Prospective Chronic Rejection Project
Bibliographic record
Abstract
An international collaborative study of 45 transplant centers was undertaken at the 14th International HLA (human leukocyte antigen) and Immunogenetics Workshop to see if HLA antibodies detected posttransplant are predictive of chronic graft failure. With the newly developed assay, MICA (major histocompatibility complex class I-related chain A) antibodies were also measured and their effect analyzed. Total of 5219 sera from patients who were more than 6 months posttransplant with functioning graft were tested for HLA antibodies by enzyme-linked immunosorbent assay, flow cytometry, or Luminex. HLA antibodies were found in 27.2% of kidney patients, 23.6% in the liver, 52.7% in the heart, and 21.7% in the lung. The method of antibody testing did not have a marked influence on the frequency of antibodies detected. MICA antibodies were detected in 15% of kidney patients, 30% of heart patients, and 31% of liver patients. Among 948 kidney patients who had HLA antibodies, 7.3% had rejected their graft within 1 year of testing, compared with 1.7% in 2615 patients without HLA antibodies (P= 0.8 x 10(-17)). Death occurred in 1.4% of total kidney patients and did not correlate to the presence of antibodies. We conclude that patients with posttransplant HLA antibodies indeed have a higher rate of chronic graft failure and that posttransplant antibodies are predictive of chronic rejection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".