Creation of a high-fidelity simulation tool to teach competency in radiation oncology treatment plan evaluation.
Bibliographic record
Abstract
11007 Background: Although treatment plan evaluation (TPE) is a core competency for radiation oncology residents, gaps in teaching exist. The purpose was to create an interactive TPE case bank for residents to improve competency. Methods: A needs assessment informed case bank development. Residents assessed their confidence in TPE using a 10-point Likert scale (1 = least, 10 = most confident). A list of clinically unacceptable plans were compiled and categorized by clinical site, reason for rejection and relevance. An interactive web-based DICOM-RT tool was used to query and interact with the case bank database. A companion software tool, acting as an interactive simulation platform, was created allowing user interaction. Results: Twenty-three participants (70%) responded to the needs assessment: 6 junior, 7 senior and 10 former residents. Opportunities for improving TPE were identified; the mean confidence scores were: target coverage assessment (6.3+2.7), doses to normal tissue (6.2+2.5), conformity (5.5 +2.6), plan acceptability (5.4+2.5) and ability to suggest improvements (4.8+2.3). Extracted themes for case bank development included incorporating diverse clinical sites, target coverage/normal tissue assessment, conformity and provision of feedback. Of the 677 clinically unacceptable plans, a final list of 50 were selected for inclusion. They were categorized, anonymized and imported. Additional (un)acceptable plans were generated to augment the case bank where required. The companion simulation software platform describes each clinical scenario and allows residents to enter their assessment and suggested corrective action (if applicable). The platform then provides immediate feedback, including error description and correction strategy. Conclusions: An innovative TPE case bank has been created to address a learning gap in radiation oncology training. The high fidelity simulation format allows case interactivity and feedback with the goal of improving TPE competency. This platform can be leveraged for teaching/assessment in competency based medical education. Future work will evaluate resident satisfaction with and effectiveness of the case bank as a learning tool.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".