Ambulatory Impedance Cardiography in Hypertension: A Validation Study
Bibliographic record
Abstract
The management of hypertension is improved by knowledge of the hemodynamics underlying blood pressure. Impedance Cardiography (ICG) provides data on a range of hemodynamic variables that affect blood pressure. However, ICG captures only fixed descriptions of hemodynamic characteristics. Improvements in ambulatory technology have led to the development of the Ambulatory Impedance Monitor (AIM) which records hemodynamic data during the activities of daily living. The purpose of this study was to evaluate the sensitivity of the AIM to detect hemodynamic changes associated with postural shift in persons with hypertension. Using a repeated measures cross-over design, sitting and standing hemodynamic measures were taken in seventeen persons with hypertension while wearing the AIM-BpTRU system designed for standard office use and the AIM-Spacelabs system designed for ambulatory monitoring. Both AIM-blood pressure monitoring systems detected significant changes from sitting to standing posture in heart rate (p=0.03), stroke volume (p=0.002), left ventricular ejection time (p<0.001), systemic vascular resistance (p=0.03) and diastolic blood pressure (p<0.001). Additionally, both systems generated measures of cardiac function that were positively correlated (p<0.001) and not significantly different (p>0.05). Our findings support previous work and demonstrate that the AIM provides valid and reliable estimates of cardiac function in persons with hypertension.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".