Sensitization in Transplantation: Assessment of Risk (STAR) 2017 Working Group Meeting Report
Bibliographic record
Abstract
The presence of preexisting (memory) or de novo donor-specific HLA antibodies (DSAs) is a known barrier to successful long-term organ transplantation. Yet, despite the fact that laboratory tools and our understanding of histocompatibility have advanced significantly in recent years, the criteria to define presence of a DSA and assign a level of risk for a given DSA vary markedly between centers. A collaborative effort between the American Society for Histocompatibility and Immunogenetics and the American Society of Transplantation provided the logistical support for generating a dedicated multidisciplinary working group, which included experts in histocompatibility as well as kidney, liver, heart, and lung transplantation. The goals were to perform a critical review of biologically driven, state-of-the-art, clinical diagnostics literature and to provide clinical practice recommendations based on expert assessment of quality and strength of evidence. The results of the Sensitization in Transplantation: Assessment of Risk (STAR) meeting are summarized here, providing recommendations on the definition and utilization of HLA diagnostic testing, and a framework for clinical assessment of risk for a memory or a primary alloimmune response. The definitions, recommendations, risk framework, and highlighted gaps in knowledge are intended to spur research that will inform the next STAR Working Group meeting in 2019.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".