High-Resolution Imaging Using the VisualSonics Vevo 2100 on Isolated, Perfused Porcine Kidneys on Mechanical Circulatory Support
Bibliographic record
Abstract
Despite many improvements in the field of renal transplantation, the key problem that persists is the lack of organs for all the patients who need kidneys. This problem continues despite the addition of extended criteria donors and donation after cardiac death. Compounding this issue is the high discard rate and there are no good means to truly predict renal function using current pretransplantation testing parameters. In an isolated renal perfusion model using porcine kidneys, we tested the proof of principle that a Vevo 2100 high-frequency high-resolution ultrasound system (Fujifilm VisualSonics, Inc., Toronto, Canada) could assess renal parenchymal perfusion and flow in the central renal vessels which could not assess by conventional ultrasound. Images and velocities were easily obtained during these studies. High-frequency ultrasound imaging may be a feasible and reproducible method for assessing renal parenchymal integrity and function pretransplantation. Further studies are required to determine the sensitivity and specificity of this approach in comparison with traditional renal biopsy pretransplantation with the goal of increasing the identification and use of donated kidneys for transplantation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".