Editorial: Future challenges and directions in determining allo-immunity in kidney transplantation
Bibliographic record
Abstract
Future challenges and directions in determining allo-immunity in kidney transplantationImproving long-term allograft survival remains one of the key contemporary challenges of transplantation medicine.Despite improvement in short-term kidney allograft outcomes, more than 1 in 2 kidney transplant recipients will lose their allograft within 15 years of transplantation (1).Returning to dialysis is associated with a substantial risk of death which is increased by almost 10-fold compared to patients with functioning kidney allografts (2).Maintaining a good functioning allograft over time is complex and multiple risk factors influence long-term allograft survival, ranging from organ procurement factors, post-transplant adverse events such as delayed graft function and rejection episodes, to the effects of chronic exposure to immunosuppression.To improve kidney allograft survival, both traditional and emerging potentially modifiable risk factors need to be identified.Another equally important aspect of transplantation medicine is the assessment of sensitization status (3, 4).Pre-transplant immunological risk assessment typically involves the screening for anti-human leukocyte antigen (HLA) antibody, which can occur following prior allograft loss, infection, pregnancy and blood transfusion.Although
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".