Examination Outcomes Following Use of Card Games for Learning Radiographic Image Quality in Veterinary Medicine
Bibliographic record
Abstract
Understanding the concepts of radiographic image quality and artifact formation can be difficult for veterinary students. Two educational card games were previously developed to help students learn about factors affecting contrast and blackness as well as radiographic artifacts. Second-year veterinary students played one of the two card games as a part of their normal studies for their veterinary imaging course and later took the radiographic physics quiz normally administered during the course. Performance on quiz questions related to each of the two games was compared between students who played each respective game and those who did not. The hypothesis was that students who played a game would perform better on related questions than those who did not play that game. For the contrast and blackness questions, students who played the associated game as part of their studies performed better than those who only studied by conventional means (mean 4.3 vs. 3.8 out of 5 points, p=.02). However, there was no significant difference in results between groups for artifacts questions (mean 4.7 vs. 4.5 out of 5 points, p=.35). Based on these results, educational game play can have benefits to student learning, but performance may be dependent on specific game objectives and play mechanics.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".