An Integrative and Case-Based Approach to the Teaching of General and Systemic Pathology
Bibliographic record
Abstract
Since 1993, the pre-clinical phase of the professional (DVM) curriculum at Michigan State University College of Veterinary Medicine has included two pathology courses in which both anatomic and clinical pathologists collaborate to teach concepts in general and systemic pathology. Topics such as inflammation, circulatory disturbances, and neoplasia are taught in this collaborative manner in the year 1 General Pathology course, and pathology of the digestive system (including liver and pancreas), urinary system, and lymphoid system are "team-taught" in the year 2 Clinical and Systemic Pathology course. We feel that this approach gives students an appreciation of the whole-body dynamics of a disease process as it occurs in bone marrow, peripheral blood, body fluids, and tissues and that it leads to a deep understanding of pathologic processes. In addition, the use of "active learning" instructional strategies grounded in case discussions further enhances students' understanding of important concepts by demonstration of practical applications and serves to generate strong interest in learning the subject matter. Integration of concepts of pathology with those taught concurrently in other courses, such as those in physiology and microbiology, is also an important component of pathology instruction in the pre-clinical curriculum.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".