Pathological and Clinical Characterization of the ‘Troubled Transplant’: Data from the DeKAF Study
Bibliographic record
Abstract
We are studying two cohorts of kidney transplant recipients, with the goal of defining specific clinicopathologic entities that cause late graft dysfunction: (1) prevalent patients with new onset late graft dysfunction (cross-sectional cohort); and (2) newly transplanted patients (prospective cohort). For the cross-sectional cohort (n = 440), mean time from transplant to biopsy was 7.5 +/- 6.1 years. Local pathology diagnoses included CAN (48%), CNI toxicity (30%), and perhaps surprisingly, acute rejection (cellular- or Ab-mediated) (23%). Actuarial rate of death-censored graft loss at 1 year postbiopsy was 17.7%; at 2 years, 29.8%. There was no difference in postbiopsy graft survival for recipients with versus without CAN (p = 0.9). Prospective cohort patients (n = 2427) developing graft dysfunction >3 months posttransplant undergo 'index' biopsy. The rate of index biopsy was 8.8% between 3 and 12 months, and 18.2% by 2 years. Mean time from transplant to index biopsy was 1.0 +/- 0.6 years. Local pathology diagnoses included CAN (27%), and acute rejection (39%). Intervention to halt late graft deterioration cannot be developed in the absence of meaningful diagnostic entities. We found CAN in late posttransplant biopsies to be of no prognostic value. The DeKAF study will provide broadly applicable diagnostic information to serve as the basis for future trials.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".