Histopathologic Clusters Differentiate Subgroups Within the Nonspecific Diagnoses of CAN or CR: Preliminary Data from the DeKAF Study
Bibliographic record
Abstract
The nonspecific diagnoses 'chronic rejection''CAN', or 'IF/TA' suggest neither identifiable pathophysiologic mechanisms nor possible treatments. As a first step to developing a more useful taxonomy for causes of new-onset late kidney allograft dysfunction, we used cluster analysis of individual Banff score components to define subgroups. In this multicenter study, eligibility included being transplanted prior to October 1, 2005, having a 'baseline' serum creatinine < or =2.0 mg/dL before January 1, 2006, and subsequently developing deterioration of graft function leading to a biopsy. Mean time from transplant to biopsy was 7.5 +/- 6.1 years. Of the 265 biopsies (all with blinded central pathology interpretation), 240 grouped into six large (n > 13) clusters. There were no major differences between clusters in recipient demographics. The actuarial postbiopsy graft survival varied by cluster (p = 0.002). CAN and CNI toxicity were common diagnoses in each cluster (and did not differentiate clusters). Similarly, C4d and presence of donor specific antibody were frequently observed across clusters. We conclude that for recipients with new-onset late graft dysfunction, cluster analysis of Banff scores distinguishes meaningful subgroups with differing outcomes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".