Biofeedback Regulation of Ultrafiltration and Dialysate Conductivity for the Prevention of Hypotension during Hemodialysis
Bibliographic record
Abstract
Intradialytic hypotension remains a frequent complication of dialysis, occurring in up to 33% of patients. We tested a fully integrated biofeedback system (the Hemocontrol system) that monitors and regulates blood volume contraction during hemodialysis. Seven hypotension prone patients were selected for the study. We conducted a prospective crossover study alternating dialysis sessions using the blood volume regulation system and standard dialysis sessions. Event free sessions were defined as dialysis sessions not requiring any therapeutic intervention for hypotension related signs or symptoms. There was a significant improvement in the number of event free sessions with blood volume regulation compared with standard dialysis (50.8% of sessions vs. 29.2%; p < 0.01). Percentages of event free sessions and mean postdialysis systolic blood pressure improved progressively over the course of the study, indicating improved hemodynamic stability over the study period. Therefore, the use of a biofeedback system to monitor and regulate blood volume during dialysis was helpful in restoring cardiovascular stability in a population of hypotension prone hemodialysis patients. Further studies are needed to confirm these preliminary results and to establish the role of blood volume regulation systems in reducing the incidence of hypotension during hemodialysis.
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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".