Bibliographic record
Abstract
Medical schools around the world now teach foundational anatomical sciences using integrated methods that have improved the content relevance in medical training and are more consistent with physicians’ clinical reasoning and practice. As stewards of one of these foundational sciences, histology experts at the University of British Columbia (UBC) medical school have embraced a renewed and integrated curriculum. Specifically, we have restructured our undergraduate medical curriculum and implemented an innovative competency‐based integration of years one and two histology with a more perceptibly clinical discipline, pathology. Results from faculty experiences, student outcomes on formative integrated examinations and educational research data from student learning surveys demonstrate the pedagogical effectiveness of our foundational science‐clinical integration. The next step is to establish how this pre‐clinical integration allows our medical students to utilize foundational science knowledge in subsequent clinical training. That is, how can we demonstrate that the relevance and application of this integration extends beyond the pre‐clinical curriculum and into the clinical years? In other words, what is integrated histology‐pathology medical education good for? This session will explore one answer to this question by describing our method of mapping histology‐pathology milestones and exit competencies to clinical knowledge and skills in a manner that illustrates the importance of integrated teaching in medical education. That is, by using milestones and exit competency‐based clinical scenarios, we are able to demonstrate how our integrated foundational (histology) and clinical (pathology) medical science approach enables students to progress to the level of clinical expertise expected of all graduating physicians. Our methods, experiences and outcomes can extend to other foundational science/clinical disciplines partnerships, and this session will prove useful for both educators and clinicians who are exploring or implementing interdisciplinary integrations in both undergraduate and post‐graduate medical training.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".