New Answers to Old Conundrums
Bibliographic record
Abstract
In Brief Antibody-mediated injury is a major cause of allograft dysfunction and loss. Antibodies to ABH(O) blood group antigens are classic mediators of ABO-incompatible graft rejection, whereas donor-specific anti-HLA antibodies and, more recently, autoantibodies are appreciated as important contributors to allograft inflammation and dysfunction. In August 2016, the International Summit of the Canadian National Transplant Research Program focused on recent advances in the field of antibody-mediated rejection. Here, we describe work presented and discussed at the meeting, with a focus on 3 major themes: the importance of (1) natural antibodies and autoantibodies, (2) tissue injury–derived exosomes and autoimmunity, (3) inflammasome activation and innate immune responses in regulating allograft inflammation and dysfunction. Finally, we explore novel areas of therapeutic intervention that have recently emerged from these 3 major and overlapping fields of transplantation research. A summary report on these cutting-edge topics, from basic to translational, that impact allograft outcomes as discussed at the recent Canadian National Transplant Research Program (CNTRP) International Summit.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".