ADAM, a hands‐on patient simulator for teaching principles of drug disposition and compartmental pharmacokinetics
Bibliographic record
Abstract
AIMS: To design, construct and validate a pharmacokinetics simulator that offers students hands-on opportunities to participate in the design, administration and analysis of oral and intravenous dosing regimens. METHODS: The Alberta Drug Administration Modeller (ADAM) is a mechanical patient in which peristaltic circulation of water through a network of silicone tubing and glass bottles creates a representation of the outcomes of drug absorption, distribution, metabolism and elimination. Changing peristaltic pump rates and volumes in bottles allows values for pharmacokinetic constants to be varied, thereby simulating differences in drug properties and in patient physiologies and pathologies. Following administration of methylene blue dye by oral or intravenous routes, plasma and/or urine samples are collected and drug concentrations are determined spectrophotometrically. The effectiveness of the simulator in enhancing student competence and confidence was assessed in two undergraduate laboratory classes. RESULTS: The simulator effectively models one- and two-compartment drug behaviour in a mathematically-robust and realistic manner. Data allow calculation of numerous pharmacokinetic constants, by traditional graphing methods or with curve-fitting software. Students' competence in solving pharmacokinetic problems involving calculations and graphing improved significantly, while an increase in confidence and understanding was reported. CONCLUSIONS: The ADAM is relatively inexpensive and straightforward to construct, and offers a realistic, hands-on pharmacokinetics learning opportunity for students that effectively complements didactic lectures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".