Feedback control of absolute blood volume: A new technical approach in hemodialysis
Bibliographic record
Abstract
INTRODUCTION: The success of automatic feedback control systems to improve hemodynamic stability by preventing relative blood volume from dropping beyond a critical value during dialysis is limited. The aim of this study was to use one of these systems for control of absolute rather than relative blood volume to prevent volume-dependent morbid events. METHODS: Dialysis was delivered by a machine providing feedback control of ultrafiltration rates, relative blood volume monitoring, and accurate bolus infusion of 240 mL of dialysate to measure absolute blood volume at the beginning of dialysis. Critical relative blood volume required by the control algorithm was calculated from absolute blood volume at the beginning and a critical absolute blood volume of 67 mL/kg. FINDINGS: In 40 stable patients, ultrafiltration was guided by blood volume using the feedback algorithm of the integrated program. Blood volume was maintained in a narrow range above the prespecified minimal value of 67 mL/kg. At the end of dialysis, absolute blood volume ranged from 67.5 to 72.5 mL/kg (69.4 ± 1.3 mL/kg). No volume-dependent intradialytic morbid event occurred. DISCUSSION: A feedback control system for relative blood volume-controlled ultrafiltration can be used for control of absolute blood volume. A prescribed target of absolute blood volume can be converted into relative blood volume, and this can subsequently be reached automatically with the integrated feedback system of the dialysis machine. Intradialytic morbid events could be considerably reduced. The whole procedure could be completely automated without altering the hardware of the dialysis device.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".