Bioreactance: A new tool for cardiac output and thoracic fluid content monitoring during hemodialysis
Bibliographic record
Abstract
Outpatient hemodialysis therapy (HD) can be associated with hemodynamic compromise. Bioreactance has recently been shown to provide accurate, noninvasive, continuous, measurements of cardiac output (CO) and thoracic impedance (Zo) from which thoracic fluid content (TFC) can be derived assuming TFC=1000/Zo. This study was designed to evaluate the changes in TFC in comparison with the traditional indices of fluid removal (FR) and to understand the trends in CO changes in HD patients. Minute-by-minute changes in TFC and CO were prospectively collected using the bioreactance system (NICOM) in HD patients of a single unit. Changes in body weight (DeltaW), hematocrit (DeltaHct), and amount of FR were also measured. Twenty-five patients (age 77 +/- 11 years) were included. The TFC decreased in all patients by an average of 5.4 +/- 7.9 kohm(-1), weight decreased by 1.48 +/- 0.98 kg, and FR averaged 2.07 +/- 1.93 L over a 3- to 4-hour HD session. There were good correlations between DeltaTFC and DeltaW (R=0.80, P<0.0001) and FR (R=0.85, P<0.0001). DeltaHct (4.13 +/- 3.42%) was poorly correlated with DeltaTFC (R=0.35, P=0.12) and FR (R=0.40, P=0.07). The regression line between FR and TFC yielded FR=1.0024-0.1985TFC; thus, a 1 kohm(-1) change of Zo correlates with an approximately 200 mL change in total body water. The change in CO (-0.52 +/- 0.49 L/min m(2)) during HD did not correlate with FR (R=0.15, P=NS). Changes in TFC represented the monitored variable most closely related to FR. CO remained fairly constant in this stable patient cohort. Further studies in high-risk patients are warranted to understand whether TFC and CO monitoring can improve HD session management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".