Half-logistic time constant: a more reliable lusitropic index than monoexponential time constant regardless of temperature in canine left ventricle
Bibliographic record
Abstract
Temperature changes influence cardiac diastolic function. The monoexponential time constant (tauE), which is a conventional lusitropic index of the rate of left ventricular (LV) pressure fall, increases with cooling and decreases with warming. We have proposed that a half-logistic time constant (tauL) is a better lusitropic index than tauE at normothermia. In the present study, we investigated whether tauL can remain a superior measure as temperature varies. The isovolumic relaxation LV pressure curves from the minimum of the first time derivative of LV pressure (dP/dtmin) to the LV end-diastolic pressure were analyzed at 30, 33, 36, 38, and 40 degrees C in excised, cross-circulated canine hearts. tauL and tauE were evaluated by curve-fitting using the least squares method and applying the half-logistic equation, P(t) = PA/[1 + exp(t/tauL)] + PB, and the monoexponential equation, P(t) = P0exp(-t/tauE) + Pinfinity. Both tauL and tauE increased significantly with decreasing temperature and decreased with increasing temperature. The half-logistic correlation coefficient (r) values were significantly higher than the monoexponential r values at the 5 above-mentioned temperatures. This implies that the superiority of the goodness of the half-logistic fit is not temperature dependent. The half-logistic model characterizes the amplitude and time course of LV pressure fall more reliably than the monoexponential model. Hence, we concluded that tauL is a more useful lusitropic index regardless of temperature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".