P.66 Radial Artery Systolic-Diastolic Pulse Transit Time After Kidney Transplantation
Bibliographic record
Abstract
Abstract Purpose/Background/Objective We have previously shown that restoration of kidney function through kidney transplantation (KTx) is associated with improved aortic stiffness. In this study, we aim to examine whether this change in aortic stiffness translates into improvement of radial artery systolic-diastolic pulse transit time. Methods Before and three months after KTx, we obtained radial pressure waveforms using applanation tonometry, in a group of 61 patients with restored renal function (eGFR > 45 ml/min/1,73 m2). Radial waveforms were recorded over a 10 seconds period and ensemble-averaged (using in house-MATLAB program) to obtain a single waveform and then modelled using two Gaussian functions, was then determined as the transit time between the first systolic peak T1 and the early diastolic peak T2. Results 61 patients (66% male, mean age: 48 ± 14 years, mean eGFR 3 months after Ktx: 66.0 ± 17.1) were assessed. After KTx, there was a significant reduction in central systolic (125,266 ± 21,848 to 108,994 ± 14,407, p < 0.001) and diastolic BP (84,718 11,679 to 74,092 9,774, p < 0.001), carotid-femoral PWV (11,444 2,626 to 10,235 1,890, p < 0.001) and carotid-radial PWV (9,350 1,485 to 8,831 1,291, p = 0,003). While T1 declined (0.184 [0.173–0.198] to 0.180 [0.168–0.194], p = 0.018), there were no significant changes in T2 (0.322 [0.295–0.360] to 0.318 [0.283–0.355], p = 0.169) and in dT1-2 (0.135 [0.119–0.161] to 0.134 [0.117–0.167], p = 0.457). Conclusions Contrary to our expectation, three months after KTx, we did not observe a significant change in radial systolic-diastolic pulse transit time after kidney transplantation, despite an improvement of BP, aortic and brachial stiffness.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".