Adjustment of target weight based on absolute blood volume reduces the frequency of intradialytic morbid events
Bibliographic record
Abstract
INTRODUCTION: Adequate volume management removing excess volume and at the same time avoiding intradialytic morbid events (IME) remains a core problem in current hemodialysis (HD) therapy. Recently, we developed a feasible method to determine absolute blood volume (Vs, in mL/kg) in patients on HD. The aim of this study was to investigate the suitability of Vs measurements for volume management. METHODS: Following a 4 week baseline phase to quantify the frequency of IME, volume status was determined in a single specified HD session during which Vs was measured using dialysate dilution, volume overload (Vo, L) was measured using bioimpedance spectroscopy, and the occurrence of IME was recorded. Target weight was then adjusted and the frequency of IME was recorded during 4 weeks of follow-up. FINDINGS: Forty-five patients participated in this study. Twenty-two (49%) patients experienced 66 IME in 12% of HD treatments during baseline. In 15 (33%) patients who experienced IME during volume assessment both Vs (60.7 ± 4.0 vs. 73.7 ± 11.3 mL/kg, P < 0.001) and Vo (1.1 ± 0.9 vs. 2.5 ± 1.8 L, P < 0.01) were significantly lower than in stable patients, respectively. The sensitivity, specificity, and accuracy of the ≤65 mL/kg Vs threshold to predict IME was 87%, 100%, and 91%, respectively. Target weight was increased (+1.5 kg) or decreased (-5 kg) in 32 patients. The frequency of IME fell to 0.9% of all HD sessions in the following 4 weeks (P < 0.001). DISCUSSION: Adjustment of target weight based on information of Vs, Vo, and IME appears as a feasible approach to reduce the frequency of IME.
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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 | 0.000 | 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".