A 2020 Banff Antibody‐mediatedInjury Working Group examination of international practices for diagnosing antibody‐mediated rejection in kidney transplantation – a cohort study
Bibliographic record
Abstract
The Banff antibody-mediated rejection (ABMR) classification is vulnerable to misinterpretation, but the reasons are unclear. To better understand this vulnerability, we evaluated how ABMR is diagnosed in practice. To do this, the Banff Antibody-Mediated Injury Workgroup electronically surveyed an international cohort of nephrologists/surgeons (n = 133) and renal pathologists (n = 99). Most providers (97%) responded that they use the Banff ABMR classification at least sometimes, but DSA information is often not readily available. Only 41.1% (55/133) of nephrologists/surgeons and 19.2% (19/99) of pathologists reported that they always have DSA results when the biopsy is available. Additionally, only 19.6% (26/133) of nephrologists/surgeons responded that non-HLA antibody or molecular transcripts are obtained when ABMR histologic features are present but DSA is undetected. Several respondents agreed that histologic features concerning for ABMR in the absence of DSA and/or C4d are not well accounted for in the current classification [31.3% (31/99) pathologists and 37.6% (50/133) nephrologist/surgeons]. The Banff ABMR classification appears widely accepted, but efforts to improve the accessibility of DSA information for the multidisciplinary care team are needed. Further clarity is also needed in Banff ABMR nomenclature to account for the spectrum of ABMR and for histologic features suspicious for ABMR when DSA is absent.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".