Chaos Attractors of Ventricular Elastance to Evaluate Cardiac Performance
Bibliographic record
Abstract
Quantitative evaluation of cardiac function is very important in the clinical application of a ventricular assist device. This article reports a new evaluating method of E max, which is the most reliable parameter to evaluate cardiac function. Fluctuation in the E max time series data was evaluated by the nonlinear mathematical analyzing method including chaos and fractal theory. Experimental goats were anesthetized with halothane inhalation, and left ventricular volume and pressure were measured with other hemodynamic parameters to evaluate E max during various drug administrations. E max was evaluated by two methods. One was the conventional pressure volume loop evaluation and the other was the parameter optimization method without left ventricular volume data. As a result, E max evaluated by the parameter optimization method correlated well with the E max with conventional PV curve. Furthermore, interesting results were obtained. There were rhythmical fluctuations in the E max time series data. By the methodology of Takens, E max time series data was embedded into the phase space and a strange attractor was observed. These results may be important when considering E max evaluation during left ventricular assistance.
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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 | 0.002 |
| 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) | 0.001 | 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".