Using GastroPlus to teach complex biopharmaceutical concepts
Bibliographic record
Abstract
Context: In response to the COVID-19 pandemic, many educational adjustments had to be made to move in-person teaching to online classrooms. This report showcases the use of the software GastroPlus in an undergraduate-level pharmacy course. Programme description: This course aimed for the students to learn how to perform a mechanistically based simulation to predict the oral absorption pattern, pharmacokinetics, and biopharmaceutics properties of compounds in humans. The computer simulation offered the opportunity to teach concepts about bioavailability providing all kinds of experience with major biopharmaceutic determinants that affect systemic drug exposure. Evaluation: The advantage of this approach was seen by the enhanced performance on the biopharmaceutics questions on the final exam compared with the previous year where the laboratory was not implemented: An increase from 2019 (where no laboratory was implemented) through 2021 incorrect scores from 52, 76 to 75%, respectively. Conclusion: There is great benefit in using computer programs and simulations as a technique to enhance active learning and to educate pharmacy students in salient aspects of biopharmaceutics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".