Improving the histologic detection of donor-specific antibody-negative antibody-mediated rejection in kidney transplants
Bibliographic record
Abstract
Emerging treatments for antibody-mediated rejection (ABMR, NEJM391 (2):122-132) have increased the importance of ABMR detection when donor-specific antibody (DSA) is negative. We addressed this issue in the Trifecta-Kidney study (ClinicalTrials.gov #NCT04239703) using 3 centralized tests in 690 kidney transplant biopsies: DSA (One Lambda Inc), blood donor-derived cell-free DNA (dd-cfDNA, Prospera™ test, Natera, Inc), and molecular biopsy assessment (MMDx). We used an "AutoBanff 2022" algorithm to model the impact of alternative DSA interpretations on the histologic diagnosis of DSA-negative ABMR following Banff guidelines, including agreement with dd-cfDNA and molecular ABMR. Lowering MFI cutoffs for DSA positivity did not improve the detection of DSA-negative ABMR. However, simply calling all DSA as positive allowed the Banff 2022 guidelines to identify 46% more ABMR cases with no measurable conventional DSA, and per net reclassification improvement increased agreement between histologic diagnoses and both dd-cfDNA (P = 7.72E-7) and molecular ABMR (P = 7.69E-7). New ABMR cases were as strongly positive for dd-cfDNA and molecular ABMR as those found using the conventional DSA interpretation. A validation set analysis using INTERCOMEX study data (ClinicalTrials.gov NCT#01299168) confirmed these findings and found that the new DSA-negative ABMR cases identified by calling all DSA-positive had the same risk for graft loss as those found with conventional DSA interpretation. Trifecta-Kidney Study ClinicalTrials.gov #NCT04239703.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".