Effectiveness of a web‐based cross‐sectional anatomy learning tool (3D‐X) at improving students’ ability to interpret CT images
Bibliographic record
Abstract
Background Residency programs are demanding an increased knowledge of cross‐sectional anatomy from entering physicians studying radiological imaging. In response to medical schools decreasing the time allotted to teaching anatomy and increasing self‐directed computer‐based learning, a web‐based learning tool (3D‐X) was developed to facilitate the study of cross‐sectional anatomy. The effectiveness of 3D‐X was assessed. Methodology Undergraduate anatomy students (n=82) were randomly assigned to two groups. Each group participated in a 50‐minute learning session prior to identifying anatomical structures in CT images of the abdomen. Group 1 (control) spent the learning session studying the structures of the abdomen from prosection in an anatomy lab. Group 2 spent the first half of the lab session studying from a prosection in the anatomy lab and the second half in the computer lab using 3D‐X. Results A two‐tailed independent t‐test revealed a significant difference in test scores (mean% ± SD) between group 1 (58.4 ± 2.50, n=41) and group 2 (72.1 ± 2.44, n=41). Qualitative analysis revealed that students found the tool useful when presented as an adjunct to traditional gross anatomy. Conclusion This study demonstrates that 3D‐X, when presented in addition to traditional gross anatomy, can improve students’ ability to identify anatomical structures in CT images, as well as improving anatomical knowledge of the abdomen. Grant Funding Source : N/A
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".