Medical Solitaire: A Flash-Based Game Facilitating Study and Review of Lecture Content. “Cysts and Tumors of the Liver”
Bibliographic record
Abstract
Abstract This module is a game-based desktop study and review tool that deals with the cysts and tumors of the liver. Medical Solitaire structures factual course content into an engaging format. The top half of the game screen can display up to eight categories (e.g., types of hepatic cysts and tumors in the illustrative example). The lower half of the game screen contains a stack of digital cards (text, laboratory data, radiographic images, gross pathology images, histopathologic images) that are to be matched with the categories above. The student evaluates the data on the top card of the stack and decides with which of the categories it matches. They then drag the card to that category (much like playing computer solitaire). A score is tabulated in the lower right corner. At the end of the game, the student receives a percent grade reflecting how well they have done in matching card data with each category. Low scores would tell the student that they need more review of the material. The categories and card stack reshuffle every time a new game is started to avoid having students remember material based solely on its location in the game. The first iteration of the tool is being published to highlight the role that game-playing can have in medical education and to obtain feedback from medical educators aiding the further development of the educational approach.
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 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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".