Real-time organ perfusion monitoring of human kidney transplants using <i>ex vivo</i> normothermic perfusion and reflectance spectroscopy
Bibliographic record
Abstract
Transplantation is the standard treatment for end-stage kidney disease but carries with it a non-trivial risk of post-operative complication. There is a need for a continuous, real-time, not additionally invasive method of monitoring organ perfusion. We present an approach to allograft perfusion monitoring using a human kidney model using ex vivo normothermic perfusion (EVNP) and custom spectroscopic optical reflectance probes. Five discarded human kidneys underwent EVNP, spectroscopic measurement and were subjected to perfusion compromising events (rejection, thrombosis or haemorrhage). Oxygenated and deoxygenated haemoglobin spectra were fitted to the spectra acquired from the kidneys in order to estimate the oxygen saturation. Average oxygen saturations before the perfusion compromising events were estimated to be higher than after (or similar in the control cases). Changes in oxygen saturation estimated from measurements made continuously were synchronized well with changes in renal blood flow index measurements. This proof of concept study proves promising in identifying a technique for continuous monitoring of perfusion and oxygenation of a transplanted kidney in vivo with minimal additional invasiveness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".