Evaluating the Integration of Pre‐Mortem Diagnostic Imaging in the Anatomical Study of Body Donors
Bibliographic record
Abstract
Introduction Medical faculties are embracing a modernistic approach to the teaching of anatomy that integrates diagnostic imaging largely through post‐mortem computed tomography (CT) scanning of body donors. Though this integration provides valuable learning opportunities, this approach still poses many challenges. Aim The purpose of this study was to assess the implementation of pre‐mortem donor‐specific imaging (DSI) on student learning and dissection experience in addition to gaining an understanding of the students' perceptions of the DSI‐anatomy integration on their preparation for physicianship. Methods Ethics approval was obtained by the McGill University Institutional Review Board. All donor imaging was acquired by legal donor consent and anonymized. Students in a fourth‐year medicine, cadaveric dissection‐based course were divided into two groups: group 1 received DSI with a relating case vignette (n=15) at the beginning of the course; group 2 received generic imaging (GI) relating to the type(s) of pathologies that their donor exhibited, though the DI was not of the donor's themselves. The GI group also received a donor‐specific case vignette (n=11) halfway through the course. A convergent, parallel mixed methods design was employed. Quantitative measures included statistical analyses of student dissection‐related assessment scores as well as student group responses to a simple study participant questionnaire. Using semi‐structured focus groups and inductive coding methods, the psychosocial aspects of the student dissection experience with or without DSI and students' perceptions on the use of DSI in the course were qualitatively explored. Results Statistically significant differences in student group responses for survey items assessing relevancy of imaging modality to student dissections, student understanding of anatomy and relevancy for future clinical practice was achieved. Students receiving DSI more positively supported the relevancy of imaging with anatomical dissection, expressed the importance of its implementation into later years of the medical curriculum in addition to suggesting integration earlier on within the medical curriculum as well. Academic assessment scores demonstrated insignificant differences and thus were not influenced by the type and timing of the imaging provided. Main themes arising from the qualitative analysis include the influence of DSI on future mindful practice, positive impact on dissection and humanization of the body donor through the combined use of DSI and case vignette. Conclusion These results demonstrate that the integration of DSI into anatomical dissection provided a positive learning experience for students. This form of imaging integration not only provided a learning tool to enhance anatomical dissection, but also allowed students to further develop characteristics and skills relating to future mindful practice. Support or Funding Information Jonathan Campbell Meakins and Family Memorial Fellowship, Centre for Medical Education McGill University This abstract is from the Experimental Biology 2019 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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".