Evaluating the impact of an integrated multidisciplinary head & neck competency-based anatomy & radiology teaching approach in radiation oncology: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Modern radiation oncology demands a thorough understanding of gross and cross-sectional anatomy for diagnostic and therapeutic applications. Complex anatomic sites present challenges for learners and are not well-addressed in traditional postgraduate curricula. A multidisciplinary team (MDT) based head-and-neck gross and radiologic anatomy program for radiation oncology trainees was developed, piloted, and empirically assessed for efficacy and learning outcomes. METHODS: Four site-specific MDT head-and-neck seminars were implemented, each involving a MDT delivering didactic and case-based instruction, supplemented by cadaveric presentations. There was no dedicated contouring instruction. Pre- and post-testing were performed to assess knowledge, and ability to apply knowledge to the clinical setting as defined by accuracy of contouring. Paired analyses of knowledge pretests and posttests were performed by Wilcoxon matched-pair signed-rank test. RESULTS: Fifteen post-graduate trainees participated. A statistically significant (p < 0.001) mean absolute improvement of 4.6 points (17.03%) was observed between knowledge pretest and posttest scores. Contouring accuracy was analyzed quantitatively by comparing spatial overlap of participants' pretest and posttest contours with a gold standard through the dice similarity coefficient. A statistically significant improvement in contouring accuracy was observed for 3 out of 20 anatomical structures. Qualitative and quantitative feedback revealed that participants were more confident at contouring and were enthusiastic towards the seminars. CONCLUSIONS: MDT seminars were associated with improved knowledge scores and resident satisfaction; however, increased gross and cross-sectional anatomic knowledge did not translate into improvements in contouring accuracy. Further research should evaluate the impact of hands-on contouring sessions in addition to dedicated instructional sessions to develop competencies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".