Increased arterial stiffness predicts cognitive impairment in hemodialysis patients
Bibliographic record
Abstract
Introduction Cognitive impairment is a major, but underdiagnosed, risk factor for negative outcomes in patients with chronic kidney disease (CKD). The main goal of this study was to evaluate, for the first time, the relationship between arterial stiffness and cognitive impairment in a cohort of hemodialysis patients. Methods We prospectively analyzed the cognitive function and pulse wave velocity (PWV) of 72 hemodialysis patients, mean age 56.54 ± 13.96 y, from two Romanian dialysis centers. We administered to all patients the Mini Mental State Examination (MMSE), Trail Making Test Part-A (TMTA) and Part-B (TMTB), and Mini-Cog Test. Radial arterial waveforms during 40 cardiac cycles were recorded in each patient. Findings Mean MMSE score was 25.13 ± 3.47, mean MiniCog score was 3.51 ± 1.18, mean TMTA (sec) was 103.77 ± 53.13 and mean TMTB (sec) was 214.93 ± 112.25. In linear unadjusted regression, PWV values were associated with worse MMSE scores (β = -0.36, P = 0.001, 95% CI: -0.68 to -0.17), and MiniCog scores (β = -0.26, P = 0.02, 95% CI: -0.19 to -0.01). Also, PWV value was significant associated with TMTA test, but not with TMTB. After further adjustments, PWV remained a strong predictor for cognitive impairment measured by MMSE, TMTA, MiniCog, but not for TMTB. Discussion Cognitive impairment was associated with higher PWV values in our cohort. Further evidence is needed in order to support arterial stiffness as a long-term predictor for cognitive decline in ESRD 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) | 0.002 | 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".