Banff Initiative for Quality Assurance in Transplantation (BIFQUIT): Reproducibility of C4d Immunohistochemistry in Kidney Allografts
Bibliographic record
Abstract
Detection of C4d is crucial for diagnosing antibody-mediated-rejection. We conducted a multicenter trial to assess the reproducibility for C4d immunohistochemistry on paraffin tissue. Unstained slides from a tissue microarray (TMA) comprising 44 kidney allograft specimens representing a full analytical spectrum for C4d were distributed to 73 institutions. Participants stained TMA slides using local protocols and evaluated their slides following the Banff C4d schema. Local staining details and evaluation scores were collected online. Stained slides were returned for centralized panel re-evaluation. Kappa statistics were used to determine reproducibility. Poor interinstitutional reproducibility was observed (kappa 0.17), which was equally due to limitations in interobserver (kappa 0.44) and interlaboratory reproducibility (kappa 0.46). Depending on the cut-off, reproducibility could be improved by omitting C4d grading and only considering ± calls. Heat-induced epitope recovery (pH 6-7, 20-30 min, citrate buffer) with polyclonal antibody incubation (<1:80, >40 min) appeared as best practice. The BIFQUIT trial results indicated that C4d staining on paraffin sections varies considerably between laboratories. Refinement of the current Banff C4d scoring schema and harmonization of tissue processing and staining protocols is necessary to achieve acceptable reproducibility.
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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 | 0.232 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".