Clinical relevance of human leukocyte antigen antibodies in liver, heart, lung and intestine transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Solid phase assays identify human leukocyte antigen (HLA) antibodies with a great sensitivity. Whether to accept or decline an organ if the virtual crossmatch is positive, when to monitor and whether to treat de-novo donor-specific antibody (DSA) posttransplant remain challenging issues for the transplant clinician. RECENT FINDINGS: Technologies that can differentiate which antibodies pose the greatest risk for antibody-mediated rejection (AMR) are evolving. Complement fixing luminex assays have been used to predict high-risk antibodies, but using these assays alone will miss some preformed antibodies. How these technologies fit into the laboratory's testing algorithm will likely need to be individualized. Posttransplant de-novo DSAs are associated with inferior outcomes. In hearts, similar to renal transplantation, acute rejection is a risk factor for developing de-novo DSA. Further data are needed to determine whether other risk factors are similar to those reported for renal transplants. Antibodies to self-antigens are increasingly recognized posttransplant and how the alloimmune response contributes to altered autoregulation is a current research focus. SUMMARY: Identification of DSA enables the clinician to make informed decisions regarding whether or not to accept an organ and if augmented immunosuppression is indicated. Monitoring for DSA posttransplant identifies recipients at a greater risk for AMR and can guide management. However, the best approach to dealing with de-novo DSA remains unclear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".