Improvement in ejection fraction by nocturnal haemodialysis in end-stage renal failure patients with coexisting heart failure
Bibliographic record
Abstract
BACKGROUND: Congestive heart failure (CHF) is an independent risk factor for mortality in the end-stage renal disease (ESRD) population. Nocturnal haemodialysis (NHD), a novel mode of renal replacement therapy, may be more effective than conventional haemodialysis in reducing intravascular volume or in removing uraemic toxins with vasoconstrictor or myocardial depressant actions, and may, therefore, improve the left ventricular (LV) systolic function of patients with coexisting cardiac and renal failure. METHODS: To test this hypothesis, we determined, in six patients (mean age+/-SD: 49.5+/-9 years), blood pressure (BP), ejection fraction (EF: radionucleotide angiography), left ventricular mass index (LVMI: echocardiography), LV fractional shortening (FS), and extracellular fluid volume (ECFV: bioelectrical impedance): before and after a mean of 3.2+/-2.1 years following conversion from conventional dialysis (3 days/week x 4 h) to NHD (6 nights/week x 8-10 h). RESULTS: There were significant reductions in systolic and mean arterial BP (138+/-10 to 120+/-9 mmHg, P=0.04; 99+/-6 to 86+/-7 mmHg, P=0.01). There was a significant increase in EF (28+/-12 to 41+/-18%, P=0.01) and a trend to greater LV FS (20+/-10 to 38+/-17%, P=0.06). Post-dialysis ECFV was not affected by dialysis mode (18.5+/-5.1 vs 18.2+/-3.5 l, P=0.76). The number of prescribed cardiovascular medications was reduced (2.2-0.7, P=0.02). CONCLUSIONS: In ESRD patients with systolic dysfunction, NHD leads to a sustained increase of EF and a reduction in the requirement for vasoactive medications in the absence of any reduction in post-dialysis ECFV.
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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 | 0.001 |
| 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 | 0.000 |
| 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".