Diagnostic Potential of Minimally Invasive Biomarkers: A Biopsy-centered Viewpoint From the Banff Minimally Invasive Diagnostics Working Group
Bibliographic record
Abstract
With recent advances and commercial implementation of minimally invasive biomarkers in kidney transplantation, new strategies for the surveillance of allograft health are emerging. Blood and urine-based biomarkers can be used to detect the presence of rejection, but their applicability as diagnostic tests has not been studied. A Banff working group was recently formed to consider the potential of minimally invasive biomarkers for integration into the Banff classification for kidney allograft pathology. We review the existing data on donor-derived cell-free DNA, blood and urine transcriptomics, urinary protein chemokines, and next-generation diagnostics and conclude that the available data do not support their use as stand-alone diagnostic tests at this point. Future studies assessing their ability to distinguish complex phenotypes, differentiate T cell-mediated rejection from antibody-mediated rejection, and function as an adjunct to histology are needed to elevate these minimally invasive biomarkers from surveillance tests to diagnostic tests.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".