A first-in-human study of quantitative ultrasound to assess transplant kidney fibrosis
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Bibliographic record
Abstract
Kidney transplantation is the optimal treatment for renal failure. In the United States, a biopsy at the time of organ procurement is often used to assess kidney quality to decide whether it should be used for transplant. This assessment is focused on renal fibrotic burden, because fibrosis is an important measure of irreversible kidney injury. Unfortunately, biopsy at the time of transplant is plagued by problems, including bleeding risk, inaccuracies introduced by sampling bias and rapid sample preparation, and the need for round-the-clock pathology expertise. We developed a quantitative algorithm, called renal H-scan, that can be added to standard ultrasound workflows to quickly and noninvasively measure renal fibrotic burden in preclinical animal models and human transplant kidneys. Furthermore, we provide evidence that biopsy-based fibrosis estimates, because of their highly localized nature, are inaccurate measures of whole-kidney fibrotic burden and do not associate with kidney function post-transplant. In contrast, we show that whole-kidney H-scan fibrosis estimates associate closely with post-transplant renal function. Taken together, our data suggest that the addition of H-scan to standard ultrasound workflows could provide a safe, rapid and easy-to-perform method for accurate quantification of transplant kidney fibrotic burden, and thus better prediction of post-transplant renal outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it