Gross anatomy as the foundation of integrated veterinary biomedical curriculum
Bibliographic record
Abstract
The curriculum reform is usually accompanied by reduced times allocated to the teaching of anatomy. During the recent review and revision of the veterinary medical curriculum at the University of Saskatchewan, we successfully pursued the argument to integrate the teaching of veterinary biomedical disciplines around the teaching of gross anatomy. The dissection of dog in a regional format is used to teach the comparative anatomy of other veterinary species, and to coordinate and integrate the teaching of histology, embryology, biochemistry and physiology. At the end of teaching of a particular region, we do integrative clinical case studies in groups of 7–8 students (3 sessions of 90 minutes each) to integrate the information from anatomy, physiology and biochemistry. At the end each case study, the whole class is brought together for a wrap‐up session. In addition to better disciplinary and professional skills learning outcomes for the students, there is better cohesion among the biomedical science faculty. Because many colleagues from clinical departments help in development and facilitation of the integrative case studies, there is better interaction and communication between the departments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".