Comparison of stroke volume measurements during hemodialysis using bioimpedance cardiography and echocardiography
Bibliographic record
Abstract
BACKGROUND: Fluid management remains a major challenge of hemodialysis (HD) care, with serious implications for morbidity and mortality. Intradialytic fluid management is typically guided by blood pressure, an indirect resultant of hemodynamics status. Direct measurements of hemodynamic parameters may improve cardiovascular outcomes by providing rational bases for intervention. We compare stroke volume (SV) measurements using a noninvasive, regional biompedance cardiography device (NiCaS) with Doppler echocardiography (Echo) in HD setting. METHODS: Stroke volumes were simultaneously measured using the devices in 17 patients receiving maintenance HD. Measurements were made during 2 weekly HD treatments, and twice within each HD treatment during the first and last hour of each treatment, for a total of 64 SV measurements. Agreement between devices was assessed using linear regression, a Pearson's correlation coefficient, and a Bland-Altman plot all adjusted for repeated measures within patients. RESULTS: Echo and NiCaS SV mean and 95% CIs were 58.0 (50.1, 65.8) and 56.7 (49.4, 64.0) mL, respectively. NiCaS SV correlated strongly with Echo SV during the first and last hours of treatments (r = 0.93, P < 0.001 and r = 0.92, P < 0.001, respectively). Linear regression of NiCaS on Echo showed a slope of 0.97, 95% CI (0.91, 1.02) which did not differ from 1, P = 0.20. A Bland-Altman plot and 4-Quadrant plot confirmed that the 2 methods produced comparable measurements. CONCLUSION: NiCaS SV measurements are similar to and strongly correlated with Echo SV measurements. This suggests that noninvasive NiCaS technology may be a practical method for measuring SV during HD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".