P.61 Impact of Kidney Transplantation on Arterial Reservoir-Wave Analysis
Bibliographic record
Abstract
Abstract Purpose/Background/Objective According to reservoir-wave approach (RWA) arterial pressure is the sum of a reservoir pressure (RP) accounting for dynamic storage and release of blood from arteries, and an excess pressure (XSP) analogous to flow. RP is the minimal left ventricular work required to generate aortic flow, while XSP corresponds to surplus cardiac workload. We have previously shown that kidney transplantation (KTx) improves aortic stiffness [1], however, by adding renal vessels to existing vascular network, KTx may increase cardiac output. Thus, we aimed to examine whether XSP increases after KTx. Methods Before and 3 months after KTx, carotid pressure waves were recorded using arterial tonometry, calibrated using brachial diastolic and mean blood pressure. Using pressure only approach, reservoir-wave analysis was used to derive RP, XSP and their integrals (RPI, XSPI). RWA parameters were compared with Wilcoxon non-parametric test using SPSS 26.0. Results 75 patients (69% male, mean age 51 ± 13 years) were assessed. Three months after KTx, both carotid RP (121.2 ± 20.7 vs 103.5 ± 15.7, p < 0.001) and RPI (11192.52 ± 2763.11 vs 9531 ± 1978, p < 0.001) decreased significantly, but carotid XSP and XSPI remained unchanged. Carotid systolic (131.0 ± 23.2 vs 114.1 ± 15.5, p < 0.001) and diastolic (83.4 ± 11.9 vs 72.8 ± 9.93, p < 0.001) blood pressures were also reduced. Conclusion KTx decreased reservoir pressure, suggesting a decrease in minimal cardiac workload. However, we did not see an increase in excess pressure or its integral, suggesting that addition of a donor renal artery does not significantly alter cardiac outflow and excess workload 3 months after KTx.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".