Design principles for developing an efficient clinical anatomy course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The exponential growth of medical knowledge presents a challenge for the medical school curriculum. Because anatomy is traditionally a long course, it is an attractive target to reduce course hours, yet designing courses that produce students with less understanding of human anatomy is not a viable option. Faced with the challenge of teaching more anatomy with less time, we set out to understand how students employ instructional media to learn anatomy inside and outside of the classroom. We developed a series of pilot programs to explore how students learn anatomy and, in particular, how they combine instructional technology with more traditional classroom and laboratory-based learning. We then integrated what we learned with principles of effective instruction to design a course that makes the most efficient use of students' in-class and out-of-class learning. Overall, we concluded that our new anatomy course needed to focus on transforming how medical students think, reason, and learn. We are currently testing the hypothesis that this novel approach will enhance the ability of students to recall and expand their base of anatomical knowledge throughout their medical school training and beyond.
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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it