AST Cutting Edge of Transplantation 2013 Meeting Report: A Comprehensive Look at B Cells and Antibodies in Transplantation
Bibliographic record
Abstract
Antibody-mediated rejection (ABMR) represents a significant clinical challenge for solid organ transplantation. Mechanistic understanding of ABMR is incomplete and diagnostic accuracy for ABMR is limited, and as a result, targeted treatment remains elusive and new treatment modalities are difficult to validate. Three hundred twenty-six participants from 15 countries met for the first Cutting Edge of Transplantation (CEOT) symposium organized by the American Society of Transplantation (AST) in Chandler, Arizona, February 14-16, 2013. During the 3-day interactive symposium, presentations, moderated poster sessions and round table discussions addressed cutting edge knowledge of B and plasma cell biology, mechanisms of antibody-mediated tissue injury, advances and limitations in ABMR diagnostics, as well as current and potential new treatment options for ABMR. The outcome of the meeting identified the following unmet needs for: (a) improved understanding of the regulation of B cell maturation and antibody response to enable targeted therapies; (b) more precise diagnostics of ABMR, including molecular pathology, risk stratification by sensitive antibody testing and monitoring of treatment effects; and (c) innovative multicenter trial designs that enhance observational power, in particular, in assessing synergistic multimodality therapies with reduced toxicities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".