Bioimpedance monitoring of cellular hydration during hemodialysis therapy
Bibliographic record
Abstract
Introduction The aim of this paper is to describe and demonstrate how a new bioimpedance analytical procedure can be used to monitor cellular hydration of End Stage Renal Disease (ESRD) patients during hemodialysis (HD). Methods A tetra-polar bioimpedance spectroscope (BIS), (UFI Inc., Morro Bay, CA), was used to measure the tissue resistance and reactance of the calf of 17 ESRD patients at 40 discrete frequencies once a minute during dialysis treatment. These measurements were then used to derive intracellular, interstitial, and intravascular compartment volume changes during dialysis. Findings The mean (± SD) extracellular resistance increased during dialysis from 92.4 ± 3.5 to 117.7 ± 5.8 Ohms. While the mean intracellular resistance decreased from 413.5 ± 11.7 to 348.5 ± 8.2 Ohms. It was calculated from these data that the mean intravascular volume fell 9.5%; interstitial volume fell 33.4%; and intracellular volume gained 20.3%. Discussion These results suggest that an extensive fluid shift into the cells may take place during HD. The present research may contribute to a better understanding of how factors that influence fluid redistribution may affect an ESRD patient during dialysis. In light of this finding, it is concluded that the rate of vascular refill is jointly determined with the rate of "cellular refill" and the transfer of fluid from the intertitial compartment into the intravascular space.
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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 | 0.000 | 0.001 |
| 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 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".