Clinical recommendations for posttransplant assessment of anti–HLA (Human Leukocyte Antigen) donor-specific antibodies: A Sensitization in Transplantation: Assessment of Risk consensus document
Bibliographic record
Abstract
Although anti-HLA (Human Leukocyte Antigen) donor-specific antibodies (DSAs) are commonly measured in clinical practice and their relationship with transplant outcome is well established, clinical recommendations for anti-HLA antibody assessment are sparse. Supported by a careful and critical review of the current literature performed by the Sensitization in Transplantation: Assessment of Risk 2022 working group, this consensus report provides clinical practice recommendations in kidney, heart, lung, and liver transplantation based on expert assessment of quality and strength of evidence. The recommendations address 3 major clinical problems in transplantation and include guidance regarding posttransplant DSA assessment and application to diagnostics, prognostics, and therapeutics: (1) the clinical implications of positive posttransplant DSA detection according to DSA status (ie, preformed or de novo), (2) the relevance of posttransplant DSA assessment for precision diagnosis of antibody-mediated rejection and for treatment management, and (3) the relevance of posttransplant DSA for allograft prognosis and risk stratification. This consensus report also highlights gaps in current knowledge and provides directions for clinical investigations and trials in the future that will further refine the clinical utility of posttransplant DSA assessment, leading to improved transplant management and patient care.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".