Impact of Augmenting Dialysis Frequency and Duration on Cardiovascular Function
Bibliographic record
Abstract
Conventional hemodialysis (CHD) only delivers 10% to 15% of renal function in a nonphysiological intermittent mode. Because it occurs nightly and is sustained over a longer dialysis time, the uremic clearance provided by nocturnal hemodialysis (NHD) far exceeds that of CHD. Increasing the dose and frequency of dialysis by NHD has been demonstrated, in both short- and long-term studies, to reverse several important risk factors for adverse cardiovascular events in patients with end-stage renal disease such as hypertension, left ventricular hypertrophy, systolic dysfunction, conduit artery stiffness, attenuated baroreflex regulation of heart rate, disturbed heart rate variability, sleep apnea, and endothelium-dependent vasodilation. In addition, the Toronto NHD experience has reported an emerging body of evidence demonstrating the benefits of NHD on anemia management, inflammation, and endothelial progenitor cell biology. The mechanism(s) by which nocturnal hemodialysis improves cardiovascular outcomes are under active investigation by our group. It is tempting to speculate that NHD has the potential to decrease endothelial/myocardial injury and restore simultaneously endothelial repair, thereby improving cardiovascular function in patients with end-stage renal disease. The objectives of the present document are (1) to review the mechanisms underlying dialysis-associated cardiovascular morbidity and (2) to describe the restorative potential of NHD on the cardiovascular system.
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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) | 0.002 | 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".