Scoring Total Inflammation Is Superior to the Current Banff Inflammation Score in Predicting Outcome and the Degree of Molecular Disturbance in Renal Allografts
Bibliographic record
Abstract
Emerging molecular analysis can be used as an objective and independent assessment of histopathological scoring systems. We compared the existing Banff i-score to the total inflammation (total i-) score for assessing the molecular phenotype in 129 renal allograft biopsies for cause. The total i-score showed stronger correlations with microarray-based gene sets representing major biological processes during allograft rejection. Receiver operating characteristic curves showed that total-i was superior (areas under the curves 0.85 vs. 0.73 for Banff i-score, p = 0.012) at assessing an abnormal cytotoxic T-cell burden, because it identified molecular disturbances in biopsies with advanced scarring. The total-i score was also a better predictor of graft survival than the Banff i-score and essentially all current diagnostic Banff categories. The exception was antibody-mediated rejection which is able to predict graft loss with greater specificity (96%) but at low sensitivity (38%) due to the fact that it only applies to cases with this diagnosis. The total i-score is able to achieve moderate sensitivities (60-80%) with losses in specificity (60-80%) across the whole population. Thus, the total i-score is superior to the current Banff i-score and most diagnostic Banff categories in predicting outcome and assessing the molecular phenotype of renal allografts.
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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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".