Randomized Crossover Trial of Blood Volume Monitoring–Guided Ultrafiltration Biofeedback to Reduce Intradialytic Hypotensive Episodes with Hemodialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intradialytic hypotension (IDH) is associated with morbidity. The effect of blood volume-guided ultrafiltration biofeedback, which automatically adjusts fluid removal rate on the basis of blood volume parameters, on the reduction of IDH was tested in a randomized crossover trial. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a 22-week, single blind, randomized crossover trial in patients receiving maintenance hemodialysis who had >30% of sessions complicated by symptomatic IDH in five centers in Calgary, Alberta, Canada. Participants underwent a 4-week run-in period to standardize dialysis prescription and dry weight on the basis of clinical examination. Those meeting inclusion criteria were randomized to best clinical practice hemodialysis (control) or best clinical practice plus blood volume-guided ultrafiltration biofeedback (intervention) for 8 weeks, followed by a 2-week washout and subsequent crossover for a second 8-week phase. The primary outcome was rate of symptomatic IDH. RESULTS: =0.29). There were no differences in the rate or proportion of sessions with asymptomatic IDH or symptoms alone. Results remained consistent when adjusted for randomization order and study week. There were no differences between intervention and control in the last study week in interdialytic weight gain (difference [SD], -0.02 [0.8] kg), brain natriuretic peptide (1460 [19,052] ng/L), cardiac troponins (3 [86] ng/L), extracellular water-to-intracellular water ratio (0.05 [0.33]), ultrafiltration rate (1.1 [7.0] ml/kg per hour), and dialysis recovery time (0.43 [19.25] hours). CONCLUSION: The use of blood volume monitoring-guided ultrafiltration biofeedback in patients prone to IDH did not reduce the rate of symptomatic IDH events.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".