Bibliographic record
Abstract
Understanding the events leading to allorecognition and the subsequent effector pathways engaged is key for the development of strategies to prolong graft survival. Optimizing patient outcomes will require 2 major advancements: (1) minimizing premature death with a functioning graft in the patients with stable graft function, and (2) maximizing graft survival by avoiding the aforementioned allorecognition. This necessitates personalized immunosuppression to avoid known metabolic side effects, risk for infection, and malignancy, while holding the alloimmune system in check. Since the beginning of transplant a key strategy to achieve this goal is to minimize HLA mismatching between donor and recipient. What has not evolved is any refinement in our evaluation of HLA relatedness between donor and recipient when HLA mismatch exists. Donor-recipient HLA mismatch at the amino acid level can now be determined. These mismatches serve as potential epitopes for de novo donor specific antibody development and correlate with late rejection and graft loss. It is in this context that HLA epitope analysis is considered as a strategy to permit safe immunosuppression minimization to improve patient outcomes through: (1) improved allocation schemes that favor donor-recipient pairs with a low HLA epitope mismatch load (especially at the class II loci) or avoiding specific epitope mismatches known to be highly immunogenic and (2) immunosuppressive minimization in patients with low epitope mismatch loads or without highly immunogenic epitope mismatches.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".