Banff Histopathological Consensus Criteria for Preimplantation Kidney Biopsies
Bibliographic record
Abstract
The Banff working group on preimplantation biopsy was established to develop consensus criteria (best practice guidelines) for the interpretation of preimplantation kidney biopsies. Digitally scanned slides were used (i) to evaluate interobserver variability of histopathologic findings, comparing frozen sections with formalin-fixed, paraffin-embedded tissue of wedge and needle core biopsies, and (ii) to correlate consensus histopathologic findings with graft outcome in a cohort of biopsies from international medical centers. Intraclass correlations (ICCs) and univariable and multivariable statistical analyses were performed. Good to fair reproducibility was observed in semiquantitative scores for percentage of glomerulosclerosis, arterial intimal fibrosis and interstitial fibrosis on frozen wedge biopsies. Evaluation of frozen wedge and core biopsies was comparable for number of glomeruli, but needle biopsies showed worse ICCs for glomerulosclerosis, interstitial fibrosis and tubular atrophy. A consensus evaluation form is provided to help standardize the reporting of histopathologic lesions in donor biopsies. It should be recognized that histologic parameters may not correlate with graft outcome in studies based on organs deemed to be acceptable after careful clinical assessment. Significant limitations remain in the assessment of implantation biopsies.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".