Mathematical model of renal elimination of fluid and small ions during hyper‐ and hypovolemic conditions
Bibliographic record
Abstract
This study is concerned with the formulation of a 'kidney module' linked to the plasma compartment of a larger mathematical model previously developed. Combined, these models can be used to predict, amongst other things, fluid and small ion excretion rates by the kidney; information that should prove useful in evaluating values and trends related to whole-body fluid balance for different clinical conditions to establish fluid administration protocols and for educational purposes. The renal module assumes first-order, negative-feedback responses of the kidney to changes in plasma volume and/or plasma sodium content from their normal physiological set points. Direct hormonal influences are not explicitly formulated in this empiric model. The model also considers that the renal excretion rates of small ions other than sodium are proportional to the excretion rate of sodium. As part of the model development two aspects are emphasized (1): the estimation of parameters related to the renal elimination of fluid and small ions, and (2) model validation via comparisons between the model predictions and selected experimental data. For validation, model predictions of the renal dynamics are compared with new experimental data for two cases: plasma overload resulting from external fluid infusion (e.g. infusions of iso-osmolar solutions and/or hypertonic/hyperoncotic saline solutions), and untreated hypo volemic conditions that result from the external loss of blood. The present study demonstrates that the empiric kidney module presented above can provide good short-term predictions with respect to all renal outputs considered here. Physiological implications of the model are also presented.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".