Can Online Teaching of Radiographic Anatomy Replace Conventional On-Site Teaching? A Randomized Controlled Study
Bibliographic record
Abstract
Different modalities such as lectures, dissections, 3D models, and online learning are used for teaching anatomy. To date, online learning has been considered a useful additional didactic tool. This study aimed to compare veterinary students’ performance in radiographic anatomy (radio-anatomy) after online or classroom-based teaching to assess the extent to which the two methods were interchangeable. Three strategies were compared in a cohort of 83 learners. Students were committed to online learning only, online learning with the use of specimen equine bones, or learning on conventional radiographs with specimen equine bones. At baseline (pre-test), scores from a mental rotation test and radio-anatomy knowledge test were similar between groups. After training (post-test), scores in mental rotation and radio-anatomy significantly increased by 6.7/40 units (95% CI: 5.2–8.2; p < .001) and 5.1/20 units (95% CI: 4.3–5.9; p < .001), respectively. There was no difference in scores for mental rotation and radio-anatomy knowledge between groups at post-test. Gender influenced the mental rotation, with men scoring significantly higher than women at pre-test ( M = 23.0, SD = 8.8 vs. M = 16.5, SD = 6.9; p = .001) and post-test ( M = 32.1, SD = 5.5 vs. M = 22.7, SD = 8.6; p < .001). However, radio-anatomy knowledge was not influenced by gender. These results suggest radio-anatomy teaching can be safely achieved with either conventional radiographs or online resources. This is of interest since, due to the COVID-19 outbreak, rapidly changing from on-site to online methods for teaching veterinary medical education proved necessary.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".