Characteristics of heart rate variability entropy and blood pressure during hemodialysis in patients with end‐stage renal disease
Bibliographic record
Abstract
Entropy (ENT) is a newly developed measure of the complexity of heart rate variability (HRV). The aim of this study was to characterize the complexity of HRV in patients with end-stage renal disease (ESRD) and to find a possible clinical utility. Healthy subjects and patients with ESRD undergoing hemodialysis (HD) were recruited. The HD population consisted of patients with and without diabetes mellitus (DM). An electrocardiogram was recorded before HD, and blood pressure was measured during HD. The coefficients of variation of R-R intervals, high- and low-frequency components, and ratio of the low- to high-frequency components were measured as variables of HRV. The ENT was used to describe the complexity of HRV. Forty-six healthy subjects and 27 HD patients participated in this study. The ENT negatively correlated with the duration of DM (p = 0.001), systolic blood pressure (p = 0.003), and mean blood pressure (p = 0.004) before a HD session. ENT in HD patients was lower than that in healthy subjects (p < 0.01). ENT in HD patients with DM was lower than that in HD patients without DM (p < 0.01). The change in systolic blood pressure (DeltaSBP) during a HD session showed high correlations to ENT and ultrafiltration rate (UFR) of the dialyzer. The following equation was obtained: DeltaSBP = 2.25 x ENT - 2.28 x UFR - 21.27 (R2 = 0.805; p < 0.0001). ENT decreased with uremic and diabetic status. ENT also represents a possible prediction of hypotension during a HD session.
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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 | 0.003 |
| 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) | 0.000 | 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".