Modelling volume‐loading hypertension with Guyton's model: role of whole body blood flow autoregulation
Bibliographic record
Abstract
Computer simulations offer a unique way to test the role of whole body blood flow autoregulation (WBFAR) in the pathogenesis of volume‐loading hypertension since it is not possible to block WBFAR experimentally. Using Guyton's large circulatory model, we analyzed, in the presence or absence of WBFAR, the hemodynamic and fluid volume changes in three classical models of volume‐loading hypertension. In the first model, hypertension was induced by reducing renal mass to 30% of normal and increasing salt intake by 6‐fold. In the presence of WBFAR, the development of hypertension (+30 mmHg) was characterized by an initial increase in cardiac output (CO) by ~30% followed by a secondary increase in vascular resistance (+25%). In the absence of WBFAR, there was a similar long‐term increase in blood pressure (BP). However, a nearly 2‐fold increase in CO and an increase in extracellular fluid volume by more than 30% were observed whereas vascular resistance decreased by 30% owing to the mechanical distension of blood vessels by the increased BP. Qualitatively similar results were obtained in two other models of volume‐loading hypertension: aldosterone infusion and Goldblatt 1 kidney ‐ 1 clip hypertension. From the simulations one may conclude that autoregulation limits the amount of fluid retention required to increase BP in order to achieve salt balance, but does not affect the final level of BP. (Supported by HL‐51971)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".