MacAnatomy Pathology Learning Tool Project
Bibliographic record
Abstract
MacAnatomy is the current online portal, which was developed for the Education Program in Anatomy at McMaster that allows students to easily access learning content. Previous analysis of student perceptions of the website have guided the development of specific pathology learning tools. Prototypes were then developed using the six themes elucidated from student interviews that were most requested: design & layout, user interface, learning approach, assessment, multimedia, and maintenance. Data was collected from 24 interviewees that had all participated in the anatomy curriculum, then those transcripts were analyzed by five independent assessors. We found that students predominantly placed value in having self‐assessments that were seamlessly integrated within their content. These could be as simple as incorporating exercises that allow for the comparison of pathological states to normal. One important aspect of increasing the appeal to students is ensuring the interface is maintained and updated, as well as having a modern aesthetic to design and layout. Another highly requested feature was to ensure that the learning tool content was relatable and relevant to course material. With the increased student demand for accessible online content, there is an opportunity for anatomical education to move and adapt towards utilizing various technological platforms. Our prototypes were developed to reflect student demand and preference in order to create an optimal learning tool that will be beneficial to both students and educators in the Anatomy Program at McMaster.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".