The Revised (2013) Banff Classification for Antibody-Mediated Rejection of Renal Allografts: Update, Difficulties, and Future Considerations
Bibliographic record
Abstract
The Banff 2013 classification (Banff 2013) for antibody-mediated rejection (ABMR) in renal allografts represents the first major revision of the original Banff classification for ABMR that was published in 2003. The main impetus for this revision was the need to include C4d-negative ABMR, although this revised classification contains a number of additional features based on findings reported from 2007 to 2013. Since its publication, several studies have examined the validity of different aspects of Banff 2013 and compared it to earlier (2003, 2007) versions of the Banff ABMR classification. Recent evidence, albeit limited, indicates that Banff 2013 represents an improvement over the previous versions, enhancing our ability to accurately diagnose cases of acute/active and chronic active ABMR on renal allograft biopsy. Molecular studies appear to justify the threshold value of glomerulitis plus peritubular capillaritis score ≥2 required by Banff 2013 for the diagnosis of C4d-negative ABMR; however, other aspects of the classification, including its overall interobserver reproducibility, the clinical significance of the category of C4d staining without evidence of rejection, and whether surrogate markers might potentially substitute for the requirement for the presence of donor-specific antibodies, require additional investigation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".