Cerebrovascular Reactivity and Cerebral Ischemia During Chronic Hemodialysis
Bibliographic record
Abstract
ABSTRACT Background Cerebral hypo‐perfusion during hemodialysis (HD) may contribute to cerebral ischemic lesions and atrophy in HD patients. Vascular disease and stiffness can impair cerebrovascular reactivity (CVR) in HD patients, placing them at higher risk for cerebral hypo‐perfusion during the hemodynamic stress of HD. We evaluated the relationship between CVR and change in cerebral perfusion during HD. Methods In a cohort of in‐center HD patients, we used hypercapnia to induce a change in cerebral blood flow velocity measured with transcranial Doppler to assess CVR. We used continuous cerebral oximetry during HD to measure a change in cerebral oxygen saturation (ScO2), calculating overall decline and the largest drop as markers of cerebral perfusion. We used multiple linear regression to assess the relationship between CVR and the ScO2‐associated endpoints. Findings We measured CVR in 42 HD patients and of those, 41 had the ScO2 measurements completed. The mean age was 58.5 (11.0) years, and most were male (90.5%, N = 38) with diabetes (59.5%, N = 25) and hypertension (87.5%, N = 36). The average CVR was 2.7 (1.6)%/mmHg. The average overall decline in ScO2 during HD was 2.2 (2.5)% and the average largest drop in ScO2 was 5.9 (2.8)%. CVR was negatively associated with both the largest drop in ScO2 (β = −0.67 95% CI [−1.20, −0.15], p = 0.01) and the overall decline in ScO2 (β = −0.62 95% CI [−1.09, −0.15], p = 0.01). Vascular disease was a risk factor for lower CVR (β = −1.21, 95% CI [−2.16, −0.26] p = 0.01). Conclusions A lower CVR increases the risk for cerebral hypo‐perfusion during HD. Impaired CVR may be an important part of the pathophysiology of ischemic brain injury and cognitive impairment in HD patients.
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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 | 0.000 | 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 | 0.001 |
| 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".