EVOLUTION OF RESERVOIR-WAVE ANALYSIS PARAMETERS AFTER KIDNEY TRANSPLANTATION
Bibliographic record
Abstract
Objective: According to reservoir-wave analysis (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. We now aim to determine how the improvement of aortic stiffness after kidney transplantation will translate on the reservoir-wave analysis parameters. We hypothesize that kidney transplantation will result in a decrease of excess pressure and of its integral. Design and method: This is a longitudinal observational study involving patients with kidney failure who were undergoing KTx. Before, 3, 6 and 24 months after KTx, carotid pressure waves were recorded using applanation 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). Results: 75 patients (69% male, mean age 51 ± 13 years) were assessed. Post-KTx, PR (121.4 ± 2.4 vs. 98.1 ± 1.5; P < 0.001) and PRI (11,192.5[10,515.7–11,869.4] P < 0.001) decrease significantly and continuously. XSP (18.8 ± 1.1 vs. 18.6 ± 1.5; P = 0.898) doesn’t decrease significantly. XSPI (390.9[331.4–450.4] P = 0.023) decreases significantly at 6 months post KTx but increases after. Conclusions: Kidney transplantation results in a continuous decrease of reservoir pressure and of its integral (RP and RPI). As opposed to our hypothesis, the excess pressure (XSP) does not decrease. XSP is analogous to flow. Therefore, it is possible that adding renal vessels to existing vascular network during KTx increases cardiac output. This could explain why we do not see a decrease of excess pressure post KTx despite reduced arterial stiffness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".