Evaluation of Cardiovascular Stress Reaction Using HPCD Method on a Beat-by-beat Basis
Bibliographic record
Abstract
In order to establish a bionic model/system in cardiovascular fields, comprehension of hemodynamics is important. In this study, a novel beat-by-beat hemodynamic system evaluation method named “beat-by-beat HPCD method” is proposed and evaluated. Gregg’s theoretically driven model of hemodynamics which was called “HPCD method” is improved by using non-invasive and beat-by-beat cardiovascular measurement of mean blood pressure and cardiac output. Continuous beat-by-beat measurements of MBP and CO were done on three healthy male subjects during three hours. In the measurement, a five minutes cold pressor test was executed in each subject and also each subject did exercise using a bicycle ergometer in five minutes and walked during 15 minutes. Measured beat-by-beat MBP and CO can derive beat-by-beat HP (hemodynamic profile) and CO (compensation deficit). Then, beat-by-beat changes clearly observed from plots on HP axis and CD axis plane. More vascular response can be observed on cold pressor and more myocardial response can be observed on ergometer exercise. During walking period, the response is intermediate between cold pressor and ergometer exercise. Finally, the proposed method can be considered as applicable to evaluate cardiovascular bionic system especially on evaluation of a person being subjected to stress. Keywords: hemodynamics; stress; cardiovascular system; hemodynamic profile and compensation deficit model
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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 | 0.001 | 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) | 0.002 | 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".