Ventricular afterload, preload, and cardiac function: Time for a paradigm shift
Bibliographic record
Abstract
Cognitive scientists have observed that having a model of how things work helps clinicians make decisions, even when the model is wrong! We present a paradigm shift of how the circulatory system works that challenges traditional meanings of preload and afterload. We argue that these terms evolved from studies on amphibian skeletal muscles, which function very differently from cardiac muscle. Even though both types of muscles share the same underlying sliding filament process for generating force, the terms preload and afterload developed for skeletal muscle are not applicable to the heart. Our approach is based on the work of Suga and Sagawa who showed that cardiac force production is generated by a time-varying elastance of the walls of the heart. By this process, ventricular walls become progressively stiffer during systole; the final stiffness is called end-systolic elastance (Ees). The elastance increases in straight lines to the maximum stiffness (Ees) whether the ventricular-outflow valves open or not. In other words, the ejecting ventricle does not face an “afterload.” We raise a new term, ejection “threshold load’. This is the pressure that needs to be overcome by the ventricle to open ventricular outflow valves and allow ejection of blood. The threshold load is the equivalent of aortic or pulmonary arterial diastolic pressure. Furthermore, unlike skeletal muscle, a preload is not needed to stretch diastolic myocardium. Rather, ventricular end-diastolic volume determines the maximum pressure that can be reached on Ees during systole. When end-diastolic volume is maximal, so is stroke volume. In summary, Ees, end-diastolic volume, and the threshold load are the three determinants of stroke volume for the ventricles. Finally, we argue that left ventricular stroke volume is determined by the stroke return and stroke volume to and from the right ventricle.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".