A mathematical model of the blood pressure response to salt intake
Bibliographic record
Abstract
Mathematical modeling is an invaluable tool for understanding the operation of complex physiological systems such as those regulating blood pressure (BP). While high salt intake has been implicated as a contributor to the development of essential hypertension, the dynamics and underlying mechanisms of salt‐induced hypertension remain unclear. Mathematical modeling of the BP response to changes in salt intake can provide better understanding of the mechanisms and time scales involved in the development of hypertension. Here we present our development of a mathematical model of the dynamics of salt‐induced hypertension. Model structure development was guided by general physiological principles combined with the need to adequately account for the dynamics of the original data. Model parameters were determined using numerical techniques and data from a number of experimental protocols involving Dahl salt‐sensitive and salt‐resistant rats. Our results suggest that salt‐induced hypertension may be modeled as a combination of several components, including (a) a rapid reversible effect of salt on BP, (b) a slow and irreversible component which may represent the effect of accumulating damage (renal lesions or vascular changes), (c) compensatory dynamics opposing BP increase. Our model may provide some insight into the etiology and epidemiology of salt related hypertension and its prevention. Funded by IRCSET.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".