Evaluating the integration of pre-mortem body donor imaging into a dissection-based medical anatomy course
Bibliographic record
Abstract
BACKGROUND: Medical faculties are currently embracing a modernistic approach to anatomical education that integrates diagnostic imaging largely through post-mortem computed tomography scanning of body donors. Post-mortem imaging, however, poses a multitude of challenges. The purpose of this study was to assess the implementation of pre-mortem donor-specific diagnostic imaging on student learning and dissection experience in addition to understanding the potential impact on students' preparation for clinical practice. METHODS: Students in a fourth-year medicine elective course were divided into groups; group 1 received pre-mortem donor-specific diagnostic imaging, while group 2 received pathology-specific diagnostic imaging, a collection of images relating to the type(s) of pathologies the donors exhibited, though not specific to the donors themselves. Both groups also received a donor-specific case vignette. A convergent, parallel mixed methods design was employed. This included integrating data from group responses to a study participant survey and students' academic assessment scores analyzed quantitatively through statistical analyses with data from focus group sessions investigating the psychosocial aspects of the student dissection experience and perceptions of the imaging use in the course analyzed qualitatively. RESULTS: As compared to students receiving pathology-specific diagnostic imaging, the quantitative results demonstrated that students receiving pre-mortem donor-specific diagnostic imaging more positively supported the relevancy of diagnostic imaging to their understanding of anatomy, valued the integration for future practice, and suggested an earlier integration within their medical curriculum. Qualitatively, two main themes were observed: the influence of diagnostic imaging integration on dissection experience and on professional mindset. Although both student groups received imaging corresponding to their body donor, consideration towards the humanistic nature of the body donor as a patient with a history was limited to student feedback from the donor- specific diagnostic imaging group. CONCLUSION: Overall the integration of pre-mortem donor-specific diagnostic imaging into anatomical dissection provided students with practical skill development, an enhanced dissection experience, and reinforced personal qualities critical for future practice.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".