A novel patchwork model used in lecture and laboratory to teach the three‐dimensional organization of mesenteries
Bibliographic record
Abstract
Anatomy teaching is seeing a decline in both lecture and laboratory hours across many medical schools in North America. New strategies are therefore needed to not only make anatomy teaching more clinically integrated, but also to implement new interactive teaching techniques to help students more efficiently grasp the complex organization of the human body. Among the difficult anatomical concepts that students struggle to understand, the anatomy of the peritoneal cavity with its complex projections of peritoneum could benefit strongly from new learning aids. In this report, an innovative teaching tool is presented to engage students during both lecture and laboratory, and help them build three-dimensional (3D) mental maps of peritoneal cavity. The model consists of a patchwork of mesenteries and gut made from colored cloth stitched together onto a T-shirt to denote the origin and outflow of each peritoneum projection. As the lecturer wears the life-size model, the students can appreciate the 3D organization of the peritoneal cavity on a living body. In addition, the T-shirt model can be used in parallel with dissection to ensure a strong reinforcement of the spatial understanding of the peritoneal cavity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".