A Survey of Current Practice for Antibody-Mediated Rejection in Heart Transplantation
Bibliographic record
Abstract
No evidence based management guidelines exist for antibody mediated rejection (AMR) in heart transplantation. The International Society for Heart and Lung Transplantation (ISHLT) recently introduced standardized pathologic based diagnostic criteria for AMR (pAMR 0-3). We evaluated international practice for the management of AMR focusing on pAMR grade, donor specific antibody (DSA) and allograft function. On-line survey data were analyzed from 184 ISHLT members (physicians-78%, surgeons-20%). The majority were from adult-transplant (84%), medium-large volume centres (transplants/year: 10-25, 61%; 25-50, 19%) across North America (60%) and Europe (26%). Irrespective of pAMR grade and DSA, 83-90% treated a drop in ejection fraction (EF≤45% or >25% decrease). In the presence of stable EF, an increasing number elected treatment for progressively severe pAMR grade (p<0.001) and for accompanying DSA (p<0.05, pAMR 1-3). Intravenous steroid was the most commonly used therapy followed by intravenous immunoglobulin (IVIG) or plasmapheresis, rituximab and thymoglobulin. Plasmapheresis and rituximab were favored for positive versus negative DSA (p<0.05). Using a threshold of ≥70% consensus among respondents, treatment for AMR may be considered for a drop in EF, asymptomatic pAMR 3 or asymptomatic pAMR 2 with DSA. Combination steroid, IVIG and plasmapheresis are suggested as initial therapies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".