P.19 Intradialytic Changes in Cerebral Blood Flow and Regional Changes in Arterial Stiffness
Bibliographic record
Abstract
Abstract Purpose/Background/Objective Cognitive decline is highly prevalent amongst end-stage renal disease (ESRD) patients and is accelerated upon initiation of hemodialysis (HD) [1]. ESRD increases aortic stiffness and blood flow pulsatility, which may damage small vessels of target organs like the brain [2]. In this pilot study, we aimed to evaluate the acute effect of HD on cerebral blood flow and its relation to arterial stiffness. Methods Before, every hour during and after HD (T0–T4), we measured cerebral flow velocity (FV) using transcranial Doppler, blood pressure (BP) via digital finger cuff (Nexfin), cardiac activity using ECG and aortic pulse wave velocity (PWV) with Mobile-O-Graph. FV pulsatility index (PI) and transit times between ECG peak and the foot of both FV and BP waveforms (cerebral dT; digital dT) were computed using in house MATLAB-based analysis. Changes during HD were evaluated with Generalized Estimating Equation models adjusting for multiple comparisons in SPSS 26.0. Results In eight participants aged 63 ± 17 y. old (4 diabetics, 3 women), peak FV decreased from baseline at T1 and T2 (−11.2 cm/s, p = 0.007; −12.2 cm/s, p < 0.001), PI decreased at T1 (0.81 to 0.77, p = 0.005), whilst minimum FV, mean BP and partial pressure of CO2 remained unchanged. Digital dT increased at T3 (0.19 to 0.22, p < 0.001) and cerebral dT increased throughout HD (T1-T4, p < 0.005), whereas aortic PWV did not change. Conclusions During hemodialysis, cerebral and digital transit times increased, suggesting decreased stiffness of small peripheral vessels, without significant changes in aortic stiffness. Reduced stiffness of cerebral arteries may partially explain decreased cerebral flow pulsatility.
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 | 0.000 | 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) | 0.010 | 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".