Teaching Oncology Residents Anatomy: A Multidisciplinary (MDT) Approach
Bibliographic record
Abstract
Radiation oncology has undergone a paradigm shift with the advent of precision radiotherapy techniques, demanding a thorough understanding of gross and radiologic anatomy for diagnostic and therapeutic applications. Complex anatomic sites present challenges for learners and are not well‐addressed in traditional postgraduate curricula. We developed a novel, MDT, hands‐on head‐and‐neck curriculum for residents and empirically assessed learning outcomes. 15 post‐graduate trainees participated in 4 MDT head‐and‐neck workshops, created collaboratively by an anatomist, radiologist, radiation oncologist, and otolaryngologist. Pre‐ and posttesting was performed to assess knowledge and accuracy of contouring, with a demographic profile survey and post‐intervention feedback survey. Paired analyses of knowledge pretests and postests were performed by Wilcoxon signed‐rank test. A statistically significant (p<0.001) mean absolute change of 4.6 points was observed between knowledge pretest and posttest scores. Contouring accuracy will be analyzed qualitatively (adequacy assessed by an expert) and quantitatively (calculating spatial overlap of participants’ contours and a gold standard through the dice similarity coefficient). Incorporating MDT anatomic workshops into the curriculum is a beneficial intervention associated with improved post‐intervention scores and resident satisfaction. Grant Funding Source : Departmental Submitted to PAEA 5/25/2012 (bqmelcher)
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".