Integration of Gross Anatomy Laboratory sessions into Medical Physics Curriculum using Segmentation and Augmented‐Reality
Bibliographic record
Abstract
Anatomy teaching to Medical Physics (MP) trainees is recognized by the Commission on Accreditation of Medical Physics Education Programs, and yet anatomy laboratory sessions have not been integrated in MP training curricula. This study outlines the development of anatomy laboratory sessions for MP trainees and serves as the initial step to developing an anatomical curriculum guideline for MP. The two objectives of this qualitative study are: (a) to explore the educational potential of integrated anatomy laboratory sessions in the MP curriculum and (b) to evaluate the benefits of interprofessional education activities between MP trainees and Radiation Oncology residents. Participants included 17 MP graduate students, 2 MP residents and 8 Radiation Oncology residents, over two years. Two 2‐hour anatomy laboratory sessions per year were organized following the respective in‐class lectures on the pelvis, head and neck and thorax. After the two laboratory sessions, participants were asked to fill out a survey reflecting on their experience in the laboratory. Both MP students and radiation oncology residents voiced an appreciation for the anatomy laboratory sessions for the consolidation and application of knowledge. Most mentioned that these sessions helped to “visualize” and “have a better understanding” of the anatomy taught in class and made them more “confident”. In terms of interprofessionalism, both the MP and the radiation oncology trainees expressed a better understanding of one another’s level of knowledge in anatomy. Both groups suggested to have more interprofessional sessions. They also suggested to build a stronger link between cadaveric anatomy and imaging anatomy through augmented reality technology. The integration of anatomy laboratory sessions into the anatomy curriculum for MP enhances trainees’ understanding of human anatomy and its pivotal role in radiation therapy treatment. Furthermore, interprofessional activities prior to clinical placement of MP students reinforce technical and professional communication. Support or Funding Information The authors would like to thank the support provided by the Centre for Medical Education Innovation and Research Seed Fund (to GPJCN).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".