Individually adjusted absolute blood volume feedback control: A promising solution for intradialytic hypotension
Bibliographic record
Abstract
INTRODUCTION: Intradialytic hypotension (IDH) remains one of the most frequent complications associated to hemodialysis (HD), frequently triggered by a reduction in absolute blood volume (ABV) not compensated by vascular refilling. A recently developed dilutional method allows routinary measurement of ABV and, by a simple algorithm, may turn blood volume monitor (BVM) guided UF (ultrafiltration) biofeedback into an ABV control, automatically adjusting UF rate to maintain ABV above a preset threshold. The aim of this study is to identify an individual critical ABV threshold and test the ability of an ABV feedback control to avoid IDH. METHODS: We studied 24 patients throughout three consecutive midweek HD treatments. ABV and blood pressure (BP) were measured every 30 min and anytime the patient referred any symptoms to identify each patient's critical ABV (ABV at the time of hypotension). A fixed bolus dilution approach at the start of HD was used to calculate ABV. Then, patients were followed through three additional HD treatments and IDH development was analyzed. FINDINGS: Seventy-one treatments performed in 24 patients. ABV monitoring showed a constant decrease as HD treatment progressed. Thirteen IDH events were observed in eight different patients, with a mean systolic BP drop in IDH treatments of 37.38 ± 4.31 mmHg and a mean adjusted ABV at hypotension of 71.07 ± 14.88 mL/kg. Critical ABV was individually set in patients prone to IDH. As expected, ABV feedback control successfully maintained ABV over preset critical ABV. IDH events were avoided in 21 out of 22 treatments performed. ABV drop was successfully reduced, as well as SBP drop (despite similar UF than prior to ABV feedback control implementation). DISCUSSION: ABV feedback control avoided IDH in 21 out of 22 treatments performed by maintaining blood volume above critical ABV, significantly reducing ABV variations without compromising prescribed UF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".