Integrated Case-Based Applied Pathology (ICAP): A Diagnostic-Approach Model for the Learning and Teaching of Veterinary Pathology
Bibliographic record
Abstract
Integrative Case-Based Applied Pathology (ICAP) cases form one component of learning and understanding the role of pathology in the veterinary diagnostic process at the Faculty of Veterinary Science, University of Sydney. It is a strategy that focuses on student-centered learning in a problem-solving context in the year 3 curriculum. Learning exercises use real case material and are primarily delivered online, providing flexibility for students with differing learning needs, who are supported by online, peer, and tutor support. The strategy relies heavily on the integration of pre-clinical and para-clinical information with the introduction of clinical material for the purposes of a logical three-level, problem-oriented approach to the diagnosis of disease. The focus is on logical diagnostic problem solving, primarily using gross pathology and histopathological material, with the inclusion of microbiological, parasitological, and clinical pathological data. The ICAP approach is linked to and congruent with the problem-oriented approach adopted in veterinary medicine and the case-based format used by one of the authors (PJC) for the teaching and learning of veterinary clinical pathology in year 4. Additionally, final-year students have the opportunity, during a diagnostic pathology rotation, to assist in the development and refinement of further ICAPs, which reinforces the importance of pathology in the veterinary diagnostic process. Evidence of the impact of the ICAP approach, based primarily on student surveys and staff peer feedback collected over five years, shows that discipline-specific learning, vertical and horizontal integration, alignment of learning outcomes and assessment, and both veterinary and generic graduate attributes were enhanced. Areas for improvement were identified in the approach, most specifically related to assistance in the development of generic teamwork skills.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".