Reproducibility of the Banff schema in reporting protocol biopsies of stable renal allografts
Bibliographic record
Abstract
BACKGROUND: There is evidence that biopsy of stable renal allografts may be of value in predicting chronic allograft nephropathy, the main cause of graft loss. However, the reproducibility of such histological evaluation has not been tested in this setting. We tested the reproducibility of the Banff schema for this purpose. METHODS: We rated acute and chronic changes in 184 protocol biopsies. Individual pathologists at two different Canadian transplant centres reported independently. RESULTS: There was agreement in 73.53, 42.86, and 77.08% of cases in assigning a diagnosis of acute rejection, borderline changes (as defined in the schema), and no acute rejection, respectively. Applying kappa statistics, there was very good agreement in making the diagnosis of acute rejection vs no acute rejection (kappa 0.77). There was good inter-observer agreement in scoring glomerulitis, intimal arteritis, interstitial infiltrates, tubulitis, and arteriolar hyalinosis. Rating chronic changes also gave good inter-observer agreement (kappa=0.53, 0.65, and 0.62, respectively, for mild, moderate, and severe chronic allograft nephropathy). Agreement on transplant glomerulopathy was, however, poor. CONCLUSIONS: We conclude that the Banff classification provides a reproducible method for the histological assessment of protocol renal allograft biopsies in stable grafts. Such biopsies may be valuable in detecting subclinical rejection and early chronic allograft nephropathy and may also be used as surrogate end-points in the evaluation of therapy to prevent the latter.
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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 | 0.180 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".