Optimal Cutoff Point for Immunoperoxidase Detection of C4d in the Renal Allograft: Results From a Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Although C4d deposition in peritubular capillaries has been identified as a strong risk factor for subsequent renal allograft loss, the optimal cutoff for the fraction of peritubular capillaries needed to establish a positive stain in formalin-fixed, paraffin-embedded material has not been defined systematically. The objective of this study was to establish the threshold for positive staining that best predicts renal outcome in renal biopsies in a multicenter study in which local and central pathologic conditions were compared. METHODS: Unstained renal biopsy slides were obtained from 296 patients. The percentage of peritubular capillaries staining positively for C4d was detected by immunoperoxidase staining. RESULTS: The percentage C4d deposition ranged from 0% to 90% with 44% (129/296) having a positive percentage of C4d staining. The median for positive cases was 25%. Local C4d+ results were reported qualitatively, with 28% recorded as positive for C4d. Using a centrally determined cutoff of 10%, tests for agreement of local and central C4d staining were fair (κ 0.40, 95% confidence interval 0.29-0.51). Raising the centrally determined cutoff to 25% or 50% did not change the κ values (0.44 and 0.41, respectively). By Cox proportional hazards model, C4d positivity (centrally determined assessment) using a cutoff of 10% was the strongest predictor of time to graft loss (hazard ratio 2.66, 95% confidence interval 1.68-4.21). Centrally determined C4d positivity correlated with Banff scores indicative of acute inflammation but not with scores indicative of fibrosis/atrophy or transplant glomerulopathy. CONCLUSIONS: Our findings indicate that C4d positivity, defined as more than or equal to 10% by immunoperoxidase, is a strong predictor of graft loss.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".