When to intervene for donor-specific antibody after heart transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Posttransplant donor-specific human leukocyte antigen (HLA) antibodies (DSA) represent a complex area in heart transplantation with nonstandardized practice and paucity of clinical data to guide optimal management. RECENT FINDINGS: De novo DSA after heart transplantation is common and associated with rejection, cardiac allograft vasculopathy, allograft failure, and mortality. Advances in methods for HLA antibody detection have enabled identification of DSA with high precision and sensitivity. The detection of HLA antibodies must, however, be interpreted within appropriate laboratory and clinical contexts; it remains unclear which DSA are associated with greatest clinical risk. Increased antibody and clinical surveillance as well as optimization of maintenance immunosuppression are required for all patients with DSA. Antibody-directed therapies are reserved for patients with allograft dysfunction or rejection. Treatment of DSA may also be considered in asymptomatic high-risk patients including those in whom DSA arise de novo posttransplant, is persistent, high titer, or complement activating. The impact of DSA reduction and removal on long-term clinical outcomes remains unknown. SUMMARY: Despite improvements in DSA detection, identification, and characterization, best therapeutic strategies are unclear. Prospective multicenter studies are needed to develop effective standardized approaches for DSA management in heart transplantation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 0.002 |
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".