Sodium, hypertension, and an explanation of the “lag phenomenon” in hemodialysis patients
Bibliographic record
Abstract
Sodium balance is precisely regulated by intake and output. The kidneys are responsible for adjusting sodium excretion to maintain balance at varying intakes. Our distant ancestors were herbivores. Their diet contained little sodium, so they developed powerful mechanisms for conserving sodium and achieving low urinary excretion. About 10,000 years ago, early humans became villagers and discovered that food could be preserved in brine. This led to increased consumption of salt. High salt intake increases extracellular volume (ECV), blood volume, and cardiac output resulting in elevation of blood pressure. High ECV induces release of a digitalis-like immunoreactive substance and other inhibitors of Na(+)-K(+)-ATPase. As a consequence, intracellular sodium and calcium concentrations increase in vascular smooth muscles predisposing them to contraction. Moreover, high ECV increases synthesis and decreases clearance of asymmetrical dimethyl-l-arginine leading to inhibition of nitric oxide (NO) synthase. High concentration of sodium and calcium in vascular smooth muscles, and decreased synthesis of NO lead to an increase in total peripheral resistance. Restoration of normal ECV and blood pressure are attained by increased glomerular filtration and decreased sodium reabsorption. In some individuals, the kidneys have difficulty in excreting sodium, so the equilibrium is achieved at the expense of elevated blood pressure. There is some lag time between reduction of ECV and normalization of blood pressure because the normal levels of Na(+)-K(+)-ATPase inhibitors and asymmetrical dimethyl-l-arginine are restored slowly. In dialysis patients, all mechanisms intended to increase renal sodium removal are futile but they still operate and elevate blood pressure. The sodium balance must be achieved via dialysis and ultrafiltration. Blood pressure is normalized a few weeks after ECV is returned to normal, i.e., when the patient reaches dry body weight. This is called the "lag phenomenon."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".