Can Bioimpedance Analysis Be Used to Estimate Absolute Blood Volume in Hemodialysis Patients?
Bibliographic record
Abstract
INTRODUCTION: Under physiological conditions, blood volume and extracellular volume are in a ratio of 1-3, even in hemodialysis patients. The question therefore arises: can blood volume be inferred from the bioimpedance analysis data? The aim of the study was to compare the blood volumes calculated from extracellular volume determined by bioimpedance analysis data to the actually measured blood volumes. METHODS: Immediately before treatment, extracellular volume and volume overload were evaluated by bioimpedance spectroscopy. The actual blood volume was determined by indicator dilution, using an on-line infusate bolus and subsequent calculation with the data from the relative blood volume monitor. Alternatively, blood volume was calculated from extracellular volume divided by 3 and compared to the measured blood volume. FINDINGS: Overall, there were no significant differences between measured (5.56 ± 1.47 L) and calculated (5.79 ± 1.30 L) blood volumes. However, intra-individually, there were very large discrepancies with a range of -1.409 to 1.450 L. Median absolute deviation was 382 mL corresponding to 6.2 mL/kg. The differences between measured and calculated blood volumes correlated significantly (r = -0.98; p < 0.001) with the blood to extracellular volume ratio. DISCUSSION: In almost half of patients, blood volume can be inferred from bioimpedance data with sufficient certainty. But the greater the deviation from the physiological blood to extracellular volume ratio of 1-3, the more the calculated blood volumes differ from the measured values. For this reason, bioimpedance data should not be used uncritically to set the ultrafiltration.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".