Computerized Image Analysis of Sirius Red–Stained Renal Allograft Biopsies as a Surrogate Marker to Predict Long-Term Allograft Function
Bibliographic record
Abstract
Chronic allograft nephropathy (CAN) is a major problem in posttransplant management. The lack of a reliable and early surrogate marker of CAN has hampered patient care and research. In this study, the Cortical Fractional Interstitial Fibrosis Volume (V(IntFib)), quantitated with computerized image analysis of Sirius Red-stained protocol biopsies, was examined as a potential surrogate for time to graft failure (TTGF) in 68 renal allograft recipients. At 6 mo posttransplant, V(IntFib) was highly correlated with TTGF (r = 0.64, P < 0.001). Both the Banff Chronic Sum and the Acute Sum Scores were also correlated with TTGF, but less strongly (r = 0.28, P < 0.02; r = 0.35, P < 0.003, respectively). As V(IntFib) was not correlated with the Banff Chronic Score, a multivariate model was created that incorporated V(IntFib) and both Acute and Chronic Banff pathology. This model was highly correlated with TTGF (r = 0.7, P < 0.0001). These findings suggest that V(IntFib) determined by computerized image analysis of Sirius Red-stained protocol biopsies at 6 mo posttransplant, with or without incorporation of Banff acute and chronic scoring, may provide an early surrogate for time to graft failure in renal allograft recipients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".