Technical aspects of HLA antibody testing
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since the landmark studies of Patel and Terasaki, pretransplant identification of donor-directed HLA alloantibodies (DSAs) has been a critical prelude to renal allograft transplantation. Pretransplant, DSAs may be an acceptable risk or an unconditional contraindication to transplantation depending on the particular donor : recipient combination. Posttransplant, DSAs are associated with episodes of acute rejection, chronic rejection, and graft loss. Thus, monitoring for such antibodies is an important aspect of patient care. RECENT FINDINGS: The development of solid-phase antibody detection assays significantly enhanced our ability to identify HLA antibodies, taking virtual crossmatching from concept to reality. At the root of these detection assays are two questions that have been asked for almost 50 years: are donor-directed HLA antibodies present and, if so, are they clinically relevant? While the technology related to solid-phase antibody detection has seemingly allowed the first question to be answered with exquisite sensitivity and specificity, can the same be said for question 2? SUMMARY: Solid-phase antibody detection assays have clear benefits over historical approaches to antibody identification, but are not flawless. In fact, the limitations of these assays are frequently ignored. Herein, the strengths and weaknesses of solid-phase antibody detection are highlighted.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".