Automatic feedback control of relative blood volume changes during hemodialysis improves blood pressure stability during and after dialysis
Bibliographic record
Abstract
Automatic feedback systems have been designed to control relative blood volume changes during hemodialysis (HD) as hypovolemia plays a major role in the development of dialysis hypotension. Of these systems, one is based on the concept of blood volume tracking (BVT). BVT has been shown to improve intra-HD hemodynamic stability. We first questioned whether BVT also improves post-HD blood pressure stability in hypotension-prone patients and second, whether BVT is effective in reducing the post-HD weight as many hypotension-prone patients are overhydrated because of an inability to reach dry weight. After a 3-week period on standard HD, 12 hypotension-prone patients were treated with two consecutive BVT treatment protocols. During the first BVT period of 3 weeks, the post-HD target weight was kept identical compared with the standard HD period (BVT-constant weight; BVT-cw). During the second BVT period of 6 weeks, we gradually tried to lower the post-HD target weight (BVT-reduced weight; BVT-rw). In the last week of each period, we studied intra-HD and 24 hr post-HD blood pressure behavior by ambulatory blood pressure measurement (ABPM). Pre- and post-HD weight did not differ between standard HD and either BVT-cw or BVT-rw. Heart size on a standing pre-dialysis chest X-ray did not change significantly throughout the study. There were less episodes of dialysis hypotension during BVT compared with standard HD (both BVT periods: p<0.01). ABPM data were complete in 10 patients. During the first 16 hr post-HD, systolic blood pressure was significantly higher with BVT in comparison with standard HD (both BVT periods: p<0.05). The use of BVT in hypotension-prone patients is associated with higher systolic blood pressures for as long as 16 hr post-HD. BVT was not effective in reducing the post-HD target weight in this patient group.
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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) | 0.001 | 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) | 0.001 | 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".