<i>The Time Traveling Toxicologist</i> : pilot data from a gamified approach to teaching medical toxicology
Bibliographic record
Abstract
INTRODUCTION: , an interactive computer game designed to teach core toxicology principles. METHODS: The gameplay consists of a point-and-click murder mystery in which the player sleuths for clues, reads and listens to brief toxicology lessons, and solves puzzles to prevent a fatal poisoning with decision-based branching logic and embedded feedback. RESULTS: Between the release date (30 October 2024) and the conclusion of the observation period (21 October 2025), the game was downloaded 525 times. Of those, 449 users (85.5%) launched the game at least once. Among these 449 players, 349 (77.5%) successfully completed the full game by solving all in-game puzzles and reaching the narrative conclusion. DISCUSSION: is inherently accessible, allowing learners from diverse geographic and institutional backgrounds to engage without financial or logistical barriers. Online distribution enables rapid, wide-scale dissemination compared to traditional educational tools. CONCLUSIONS: represents a novel attempt to deliver medical toxicology education through an interactive, story-driven digital game. The high levels of user engagement and completion, combined with positive subjective feedback, support its promise as an educational tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".