Bibliographic record
Abstract
During the contraction phase of the myocardium the cardiac muscle reaches its maximum state of activation near end-systole at point (Pm, Vm) on the PV-loop. The tangent to the PV-loop at (Pm, Vm) is known as the end-systolic pressure-volume relation (ESPVR). An important feature of this study is the introduction of the active pressure generated by the myocardium in the mathematical formalism describing the ESPVR (also known as the isovolumic pressure Piso by physiologists). This mathematical formalism is used with the aim of deriving useful relations between the ejection fraction (EF), the parameters describing the ESPVR, and the areas under the ESPVR. These relations give new insight in the mechanics of ventricular contraction, it is shown that the EF is just one of several indexes that can be used in order to quantify and describe the ventricular function. Also bivariate (or multivariate) analysis of data appears to be superior to univariate analysis of data in order to assess the state of the myocardium. Criteria to study the problem of heart failure with preserved ejection fraction (HFpEF) are derived and discussed. Attention is also given to the way the ventricular geometry affects the calculation of the EF. Applications to clinical data are presented that show the consistency of the mathematical formalism used.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.025 |
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".