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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".