Long-term follow-up of the DeKAF cross-sectional cohort study
Bibliographic record
Abstract
The DeKAF study was developed to better understand the causes of late allograft loss. Preliminary findings from the DeKAF cross-sectional cohort (with follow-up < 20 months) have been published. Herein, we present long-term outcomes in those recipients (mean follow-up ± SD, 6.6 ± 0.7 years). Eligibility included being transplanted prior to October 1, 2005; serum creatinine ≤ 2.0 mg/dL on January 1, 2006; and subsequently developing new-onset graft dysfunction leading to a biopsy. Mean time from transplant to biopsy was 7.5 ± 6.1 years. Histologic findings and DSA were studied in relation to postbiopsy outcomes. Long-term follow-up confirms and expands the preliminary results of each of 3 studies: (1) increasing inflammation in area of atrophy (irrespective of inflammation in nonscarred areas [Banff i]) was associated with increasingly worse postbiopsy death-censored graft survival; (2) hierarchical analysis based on Banff scores defined clusters (entities) that differed in long-term death-censored graft survival; and (3) C4d-/DSA- recipients had significantly better (and C4d+/DSA+ worse) death-censored graft survival than other groups. C4d+/DSA- and C4d-/DSA+ had similar intermediate death-censored graft survival. Clinical and histologic findings at the time of new-onset graft dysfunction define high- vs low-risk groups for long-term death-censored graft survival, even years posttransplant. These findings can help differentiate groups for potential intervention studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".