Measurement of Cerebral Blood Flow Using Transcranial Doppler Ultrasonography: Comparison of Two Populations of Patients on Chronic Hemodialysis
Bibliographic record
Abstract
Background: Patients on hemodialysis (HD) experience systemic and cerebral hemodynamic changes that are associated with cognitive impairment. The objective of this study was to quantify and compare hemodynamic changes at the cerebral level during an HD session in patients with AVF vs. those with a high-flow catheter (HFC). Methods: Cross-sectional study, controls were sought for AVF patients matched for age, sex, and time on HD. Three measurements of mean cerebral blood flow velocity (CBFV) were performed using transcranial Doppler ultrasound (during the first 15 minutes,120 and 240 minutes after HD). Other interventions were carried out before and after HD, including measurement of cardiac output (CO), cognitive evaluation using the Montreal Cognitive Assessment (MoCA), and measurement of AVF flow velocity. Results: Fifty patients were included, 20 women, average age 56 years, time on HD 3.2 years, 25 patients with AVF (50%). The groups were not different in their baseline characteristics. All patients presented a drop in CBFV, on average -26.5%, with the drop being greater in AVF vs. HFC (-28.2% vs -21.2%, p=0.27). The change in CO pre and post HD was also greater in those with AVF vs. HFC (-12.5% vs -7.6%, p=0.73). Regarding mean arterial pressure (MAP), the change was -11.3% in patients with AVF and -9.4% in HFC (p=0.90). (Fig. 1). There was no difference in MoCA score between the groups. Conclusion: All patients studied presented a significant decrease in cerebral blood flow, CO and MAP during the HD session. Interestingly, these changes are more pronounced in patients with AVF, but no statistically significant difference was observed between the study groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".