Correlation between intradialytic blood pressure variability and cognitive impairment in patients on maintenance hemodialysis
Bibliographic record
Abstract
BACKGROUND: The relationship between intradialytic blood pressure variability (BPV) and mild cognitive impairment (MCI) in maintenance hemodialysis (MHD) patients is currently unclear. Our present study aimed to illustrate the correlation between intra-dialysis BPV and CI in MHD patients. METHODS: Intradialytic SBP within 3 months before cognitive assessment of the patients were collected as baseline data and averaged as final data. The intradialytic SBP was converted to the following 4 candidate short-term BPV indices: standard deviation (SD), coefficient of variation (CV), average real variability (ARV), RANGE. Overall cognitive function was assessed by the Montreal Cognitive Assessment (MoCA) scale. RESULTS: The study finally enrolled 170 patients with 6662 dialysis records and 26,580 SBP measurements. The mean age of the patients was 57.99 years, the MCI prevalence was 78.24%. Intradialytic SBP ARV (average real variability) was notably higher in patients with MCI than in the non-MCI (NMCI) group (8.91 vs. 7.60, P = 0.042), but there was no statistical difference in the mean SBP and other BPV indices between the two groups. There was a non-linear relationship between SBP ARV and MCI, and the inflection point of SBP ARV was 7.52. CONCLUSION: Our study found that high SBP ARV was closely associated with MCI, indicating that high SBP ARV may act as an indicator of MCI in MHD patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".