A Tale of Two Approaches: Results from Integrating Medical Histology and Pathology
Bibliographic record
Abstract
In response to recommendations for curricular reform, many medical and allied health professional programs have reorganized discipline‐based courses and reduced educational contact hours. As with other core foundational medical science faculty, histology educators are navigating this reduced time for didactic lecture and lab‐based teaching. The resulting challenges are driving the development and implementation of innovative approaches to provide effective histology teaching. One approach is to integrate histology with other disciplines to both optimize limited teaching hours and increase the clinical and functional relevance of medical microanatomy. In this presentation, we describe educational studies of two different approaches to integrating histology with pathology – one in a fully integrated two year basic/clinical sciences curriculum, and one in a spiral curriculum, in which the first year focuses on basic sciences and the second year focuses on clinical sciences. The first presenter (Dr. Pinder) will share experiences from collaborations between foundational scientists and clinicians in the integration of histology and pathology. Part of a medical curriculum renewal project and now in its third iteration, this study occurs in a combined basic and clinical sciences curriculum in which histology and pathology are integrated with increasing complexity throughout years one and two. Methods include the development of integrated learning laboratories in which students first explore the normal histology of an organ and then compare and contrast it to changes in cellular morphologies and/or tissue architecture occurring in prototypical pathologies. Results include faculty experiences, student outcomes on formative integrated examinations, and educational research data from student learning surveys. The second presenter will share results from an action research study developed to identify disciplinary differences in how histology (normal and abnormal) is approached, understood, described, and taught. The faculty members, a histologist (Dr. Eastwood) and pathologist (Dr. Selinfreund), then apply this knowledge to create “transitional” learning activities to help students understand the relationship between normal and abnormal histology and navigate different disciplinary ways of knowing characteristics of histology and pathology. Research methods include qualitative analysis of faculty written reflections, debrief sessions, and collaborative teaching sessions. Results include identification of disciplinary disconnects, such as what “symmetry” means, histology knowledge most critical to histopathology, such as recognizing hematopoietic cells, and effective “transitional” activities, such as pathology integrations during histology sessions and focused histology reviews during pathology sessions. The experiences and outcomes discussed will demonstrate efficacious approaches for integrating foundational sciences and clinical disciplines and will be useful for medical science educators who are exploring or implementing interdisciplinary undergraduate medical education in different curricular settings. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".