Alternative Approaches to the Assessment of the Systemic Circulation and Left Ventricular Performance: A Proof-of-Concept Study
Bibliographic record
Abstract
Background The purpose of this article is to examine the systemic circulation and left ventricular (LV) performance by alternative, nonconventional approaches: systemic vascular conductance ( G SV ) and the head-capacity relation (ie, the relation between LV pressure and cardiac output), respectively; in so doing, we aspired to present a novel and improved interpretation of integrated cardiovascular function. Methods In 16 open-chest, anaesthetized pigs, we measured LV pressure ( P LV ), central aortic pressure ( P Ao ), and central venous pressure ( P CV ) and aortic flow ( Q Ao ). We calculated heart rate (HR), stroke volume, cardiac index (CI = cardiac output/body weight), mean P LV ( P ¯ LV ) , and the average arteriovenous pressure difference ( Δ P = P ¯ Ao − P ¯ CV ); G SV = CI /( P ¯ Ao − P ¯ CV ). We studied the effects of changing loading conditions with the administration of phenylephrine ( Δ P ¯ Ao ≥ +25 mm Hg), isoproterenol ( ΔHR ∼+25%), sodium nitroprusside ( Δ P ¯ Ao ≥ −25 mm Hg), and proximal aortic constriction (to maximize developed P LV and minimize Q Ao ). Results Sodium nitroprusside and isoproterenol increased G SV compared with phenylephrine and constriction. A maximum head-capacity curve was derived from pooled data using nonlinear regression on the maximum P ¯ LV values in Q Ao bins 12.5 mL/min/kg wide. The head-capacity relation and the plots of conductance were combined using CI as a common axis, which illustrated that CI is the output of the heart and the input of the circulation. Conclusions Thus, at a given CI , G SV determines the driving pressure and, thereby, P Ao . We also demonstrated how decreases in G SV compensate for arterial hypotension by restoring the arteriovenous pressure difference and arterial pressure.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".