Hemodynamic assessment of diastolic function for experimental models
Bibliographic record
Abstract
Traditionally, the evaluation of cardiac function has focused on systolic function; however, there is a growing appreciation for the contribution of diastolic function to overall cardiac health. Given the emerging interest in evaluating diastolic function in all models of heart failure, there is a need for sensitivity, accuracy, and precision in the hemodynamic assessment of diastolic function. Hemodynamics measure cardiac pressures in vivo, offering a direct assessment of diastolic function. In this review, we summarize the underlying principles of diastolic function, dividing diastole into two phases: 1) relaxation and 2) filling. We identify parameters used to comprehensively evaluate diastolic function by hemodynamics, clarify how each parameter is obtained, and consider the advantages and limitations associated with each measure. We provide a summary of the sensitivity of each diastolic parameter to loading conditions. Furthermore, we discuss differences that can occur in the accuracy of diastolic and systolic indices when generated by automated software compared with custom software analysis and the magnitude each parameter is influenced during inspiration with healthy breathing and a mild breathing load, commonly expected in heart failure. Finally, we identify key variables to control (e.g., body temperature, anesthetic, sampling rate) when collecting hemodynamic data. This review provides fundamental knowledge for users to succeed in troubleshooting and guidelines for evaluating diastolic function by hemodynamics in experimental models of heart failure. Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/assessment-of-diastolic-function/ .
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".