Variability of pulse wave velocity and mortality in chronic hemodialysis patients
Bibliographic record
Abstract
We have already demonstrated that in chronic hemodialysis (HD) patients, the cyclic variations in both hydration status and blood pressure are responsible for changes in pulse wave velocity (PWV). The aim of this study is to verify whether the cyclic variation of PWV influences mortality in dialysis patients. We studied 167 oligoanuric (urinary output <500 mL/day) patients on chronic standard bicarbonate HD for at least 6 months. They performed 3 HD sessions of 4 hours per week. Patients were classified into 3 groups: normal PWV before and after dialysis (LL); high PWV before and normal PWV after dialysis (HL); and high PWV before and after dialysis (HH). The carotid-femoral PWV was measured with an automated system using the foot-to-foot method. Analysis of variance was used to compare the different groups. The outcome event studied was all-cause mortality and cardiovascular mortality. The PWV values observed were LL in 44 patients (26.3%); HL in 53 patients (31.8%); and HH in 70 patients (41.9%). The 3 groups of patients are homogenous for sex, age, and blood pressure. The HH group had a higher prevalence of (P<0.001) ASCVD. It is interesting that the distribution of patients in the 3 groups is correlated with the basal value of PWV. In fact, when the basal measure of PWV is elevated, there is a higher probability that an HD session cannot reduce PWV (<12 ms). A total of 53 patients (31.7%) died during the follow-up of 2 years: 5 patients in the LL group (11.4%); 16 in the HL group (30.2%); and 32 in the HH group (50.7%) (LL vs. HL, P=0.047; LL vs. HH, P<0.00001; HL vs. HH, P=0.034). We evidence for the first time that different behaviors of PWV in dialysis subjects determine differences in mortality.
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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 | 0.001 |
| 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".