Inflammation in areas of fibrosis: The DeKAF prospective cohort
Bibliographic record
Abstract
Inflammation in areas of fibrosis (i-IFTA) in posttransplant biopsy specimens has been associated with decreased death-censored graft survival (DC-GS). Additionally, an i-IFTA score ≥ 2 is part of the diagnostic criteria for chronic active TCMR (CA TCMR). We examined the impact of i-IFTA and t-IFTA (tubulitis in areas of atrophy) in the first biopsy for cause after 90 days posttransplant (n = 598); mean (SD) 1.7 ± 1.4 years posttransplant. I-IFTA, present in 196 biopsy specimens, was strongly correlated with t-IFTA, and Banff i. Of the 196, 37 (18.9%) had a previous acute rejection episode; 96 (49%) had concurrent i score = 0. Unlike previous studies, i-IFTA = 1 (vs 0) was associated with worse 3-year DC-GS: (i-IFTA = 0, 81.7%, [95% CI 77.7 to 85.9%]); i-IFTA = 1, 68.1%, [95% CI 59.7 to 77.6%]; i-IFTA = 2, 56.1%, [95% CI 43.2 to 72.8%], i-IFTA = 3, 48.5%, [95% CI 31.8 to 74.0%]). The association of i-IFTA with decreased DC-GS remained significant when adjusted for serum creatinine at the time of the biopsy, Banff i, ci and ct, C4d and DSA. T-IFTA was similarly associated with decreased DC-GS. Of these indication biopsies, those with i-IFTA ≥ 2, without meeting other criteria for CA TCMR had similar postbiopsy DC-GS as those with CA TCMR. Those with i-IFTA = 1 and t ≥ 2, ti ≥ 2 had postbiopsy DC-GS similar to CA TCMR. Biopsies with i-IFTA = 1 had similar survival as CA TCMR when biopsy specimens also met Banff criteria for TCMR and/or AMR. Studies of i-IFTA and t-IFTA in additional cohorts, integrating analyses of Banff scores meeting criteria for other Banff diagnoses, are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".