Integrating anatomy training into radiation oncology residency: Considerations for developing a multidisciplinary, interactive learning module for adult learners
Bibliographic record
Abstract
Radiation oncologists require an in-depth understanding of anatomical relationships for modern clinical practice, although most do not receive formal anatomy training during residency. To fulfill the need for instruction in relevant anatomy, a series of four multidisciplinary, interactive learning modules were developed for a cohort of radiation oncology and medical physics residents. Instructional design was based on established learning theories, with the intent of integrating knowledge of specific anatomical regions with radiology and radiation oncology practice. Each session included presentations by a radiologist and a radiation oncologist, as well as hands-on exploration of anatomical specimens with guidance from anatomists. Pre- and post-tests distributed during each session showed significant short-term knowledge retention. According to qualitative surveys and exit interviews, participants felt more comfort' with delineating structures, gross anatomy, and radiograph interpretation at the end of each session. Overall participant experience was positive, and the modules were considered effective for learning radiologic anatomy. Suggestions for future interventions include more time, increased clinical application, additional contouring practice and feedback, and improved coordination between each of the three disciplines. Results and conclusions from this study will be used to inform the design of a future multi-day national workshop for Canadian radiation oncology residents.
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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 | 0.005 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".