Development and Application of an Interactive Neuropathology iBook as a Complementary Learning Tool for Veterinary Medicine Students
Bibliographic record
Abstract
Neuropathology is a challenging subject for most medical students. Delivering a gamified learning tool on this topic may motivate students and increase knowledge in the discipline. We report on the development of such a tool in iBook format for the systemic pathology course delivered at Ross University School of Veterinary Medicine. Composed of 10 chapters (cases), this Neuropathology iBook (NP-iB) reviews basic clinical manifestations, neuropathology, and pathogenesis of common non-neoplastic conditions that cause brain disease in domestic animals. The goal of each chapter is to reach a diagnosis by interactively answering specific questions after reviewing relevant medical history, clinical pathology, and autopsy findings. Our hypothesis: the NP-iB improves students’ ability to recognize these diseases, reflected in higher test scores. Using a post-test-only control group design, data were gathered from all students enrolled in the course in two different semesters, fall 2017 and spring 2018 ( n = 300). NP-iB users ( n = 51, 17%) and usage time were identified by answers to multiple-choice questions embedded in the course assessments. Ninety percent of users had a positive perception although no statistically significant differences were found in median test scores between users and non-users. Statistically significant test score differences were found across how much time students used the NP-iB ( p = .005); the lowest test score median values were found for neuropathology questions not related to the NP-iB in students who used it for more than 3 hours. Unexpectedly, a low number of students preferred this digital learning tool, and its use did not improve their learning outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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; 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".