Banff survey on antibody-mediated rejection clinical practices in kidney transplantation: Diagnostic misinterpretation has potential therapeutic implications
Bibliographic record
Abstract
The aim of this study was to determine how the Banff antibody-mediated rejection (ABMR) classification for kidney transplantation is interpreted in practice and affects therapy. The Banff Antibody-Mediated Injury Workgroup electronically surveyed clinicians and pathologists worldwide regarding diagnosis and treatment for 6 case-based scenarios. The participants' (95 clinicians and 72 renal pathologists) assigned diagnoses were compared to the Banff intended diagnoses (reference standard). The assigned diagnoses and reference standard differed by 26.1% (SD 28.1%) for pathologists and 34.5% (SD 23.3%) for clinicians. The greatest discordance between the reference standard and clinicians' diagnosis was when histologic features of ABMR were present but donor-specific antibody was undetected (49.4% [43/87]). For pathologists, the greatest discordance was in the case of acute/active ABMR C4d staining negative in a positive crossmatch transplant recipient (33.8% [23/68]). Treatment approaches were heterogeneous but linked to the assigned diagnosis. When acute/active ABMR was diagnosed by the clinician, treatment was recommended 95.3% (SD 18.4%) of the time vs only 77.7% (SD 39.2%) of the time when chronic active ABMR was diagnosed (P < .0001). In conclusion, the Banff ABMR classification is vulnerable to misinterpretation, which potentially has patient management implications. Continued efforts are needed to improve the understanding and standardized application of ABMR classification in the transplant community.
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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 | 0.036 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".