Estimation of fluid status using three multifrequency bioimpedance methods in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Segmental eight-point bioimpedance has been increasingly used in practice. However, whether changes in bioimpedance analysis components before and after hemodialysis (HD) using this technique in a standing position is comparable to traditional whole-body wrist-to-ankle method is still unclear. We aimed to investigate the differences between two eight-point devices (InBody 770 and Seca mBCA 514) and one wrist-to-ankle (Hydra 4200) in HD patients and healthy subjects in a standing position. METHODS: Thirteen HD patients were studied pre- and post-HD, and 12 healthy subjects once. Four measurements were performed in the following order: InBody; Seca; Hydra; and InBody again. Electrical equivalent models by each bioimpedance method and the fluid volume estimates by each device were also compared. FINDINGS: Overall, total body water (TBW) was not different between the three devices, but InBody showed lower extracellular water (ECW) and higher intracellular water (ICW) compared to the other two devices. When intradialytic weight loss was used as a surrogate for changes in ECW (∆ECW) and changes in TBW (∆TBW), ∆ECW was underestimated by Hydra (-0.79 ± 0.89 L, p < 0.01), InBody (-1.44 ± 0.65 L, p < 0.0001), and Seca (-0.32 ± 1.34, n.s.). ∆TBW was underestimated by Hydra (-1.14 ± 2.81 L, n.s.) and InBody (-0.52 ± 0.85 L, p < 0.05) but overestimated by Seca (+0.93 ± 3.55 L, n.s.). DISCUSSION: Although segmental eight-point bioimpedance techniques provided comparable TBW measurements not affected by standing over a period of 10-15 min, the ECW/TBW ratio appeared to be significantly lower in InBody compared with Seca and Hydra. Results from our study showed lack of agreement between different bioimpedance devices; direct comparison of ECW, ICW, and ECW/TBW between different devices should be avoided and clinicians should use the same device to track the fluid status in their HD population in a longitudinal direction.
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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 | 0.001 | 0.001 |
| 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 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".