The Multiple Indicator Dilution Method and Its Utility in Risk Assessment
Bibliographic record
Abstract
The multiple-indicator dilution (MID) technique entails the injection of a mixture of labeled indicators into the blood vessel immediately at the entrance of an organ, e.g., the liver, kidney, heart, or lung, and characterization of outflow dilution profiles from timed venous samples. The mathematical basis of the method encompasses linear systems of partial differential equations that are formulated for flow- or barrier-limited transport combined with intracellular metabolism/excretion. The concept can be generalized to include metabolites. MID experiments are useful for determining tissue partition coefficients as well as kinetic parameters such as membrane permeabilities or metabolic/excretory intrinsic clearances, factors that affect the mean residence times or exposure of solutes to the organ. The main utility of the MID method lies in its role in identifying the basic mechanisms of the interaction of organs with vascular components. The concentration dependence in transport and removal is revealed by the rate coefficients upon varying the input concentrations of unlabeled substances into the organ at steady state. The data obtained with MID experiments can be incorporated into physiologically based pharmacokinetic (PBPK) models such as those used for biological risk assessment. This is especially pertinent in the case where diffusional barriers appear within organs. The insight gained from the MID organ approach may be useful for PBPK models with more realistic representation of organ kinetics.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".