The Banff 2017 Kidney Meeting Report: Revised diagnostic criteria for chronic active T cell–mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials
Bibliographic record
Abstract
The kidney sessions of the 2017 Banff Conference focused on 2 areas: clinical implications of inflammation in areas of interstitial fibrosis and tubular atrophy (i-IFTA) and its relationship to T cell-mediated rejection (TCMR), and the continued evolution of molecular diagnostics, particularly in the diagnosis of antibody-mediated rejection (ABMR). In confirmation of previous studies, it was independently demonstrated by 2 groups that i-IFTA is associated with reduced graft survival. Furthermore, these groups presented that i-IFTA, particularly when involving >25% of sclerotic cortex in association with tubulitis, is often a sequela of acute TCMR in association with underimmunosuppression. The classification was thus revised to include moderate i-IFTA plus moderate or severe tubulitis as diagnostic of chronic active TCMR. Other studies demonstrated that certain molecular classifiers improve diagnosis of ABMR beyond what is possible with histology, C4d, and detection of donor-specific antibodies (DSAs) and that both C4d and validated molecular assays can serve as potential alternatives and/or complements to DSAs in the diagnosis of ABMR. The Banff ABMR criteria are thus updated to include these alternatives. Finally, the present report paves the way for the Banff scheme to be part of an integrative approach for defining surrogate endpoints in next-generation clinical trials.
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 | 0.081 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".