Sensitization in transplantation: Assessment of risk (STAR) 2019 Working Group Meeting Report
Bibliographic record
Abstract
The purpose of the STAR 2019 Working Group was to build on findings from the initial STAR report to further clarify the expectations, limitations, perceptions, and utility of alloimmune assays that are currently in use or in development for risk assessment in the setting of organ transplantation. The goal was to determine the precision and clinical feasibility/utility of such assays in evaluating both memory and primary alloimmune risks. The process included a critical review of biologically driven, state-of-the-art, clinical diagnostics literature by experts in the field and an open public forum in a face-to-face meeting to promote broader engagement of the American Society of Transplantation and American Society of Histocompatibility and Immunogenetics membership. This report summarizes the literature review and the workshop discussions. Specifically, it highlights (1) available assays to evaluate the attributes of HLA antibodies and their utility both as clinical diagnostics and as research tools to evaluate the effector mechanisms driving rejection; (2) potential assays to assess the presence of alloimmune T and B cell memory; and (3) progress in the development of HLA molecular mismatch computational scores as a potential prognostic biomarker for primary alloimmunity and its application in research trial design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".