The logistics of facilitating a dissection based anatomy curriculum in a distributed medical program
Bibliographic record
Abstract
When the University of British Columbia (UBC) medical school began a distributed model for medical undergraduate education in 2004, the delivery of gross anatomy teaching needed to adapt to these new circumstances. The pedagogical decision to continue with a dissection – based anatomy curriculum, supplemented with selected prosections was made. Core gross anatomy teaching was front‐loaded into the curriculum during the first term when all students are based at the Vancouver site. Thereafter, all cadaveric material is shipped to the satellite sites so that students can continue with their anatomy education remotely. As the Body Donation Program at UBC's Vancouver campus is the only program of its kind in British Columbia, all cadavers for the distributed program are processed at UBC. The distributed medical education program has been operational for five years, and while successful, there have been some logistical and operational challenges: The acquisition of cadaveric material. The complexities of tracking and monitoring the anatomical material between all three sites. The processing and maintenance of anatomical material. The set up and monitoring of technical audio‐visual (AV) equipment in the lab.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".