<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.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".