End-systolic Pressure–Volume Relation, Ejection Fraction, and Heart Failure: Theoretical Aspect and Clinical Applications
Bibliographic record
Abstract
A mathematical formalism describing the nonlinear end-systolic pressure-volume relation (ESPVR) is used to derive new indexes that can be used to assess the performance of the heart left ventricle by using the areas under the ESPVR (units of energy), the ordinates of the ESPVR (units of pressure), or from slopes of the curvilinear ESPVR. New relations between the ejection fraction (EF) and the parameters describing the ESPVR give some insight into the problem of heart failure (HF) with normal or preserved ejection fraction. Relations between percentage occurrence of HF and indexes derived from the ESPVR are also discussed. When ratios of pressures are used, calculation can be done in a noninvasive way with the possibility of interesting applications in routine clinical work. Applications to five groups of clinical data are given and discussed (normal group, aortic stenosis, aortic valvular regurgitation, mitral valvular regurgitation, miscellaneous cardiomyopathies). No one index allows a perfect segregation between all clinical groups, it is shown that appropriate use of two indexes (bivariate analysis) can lead to better separation of different clinical groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".