Self-directed learning module for radiation therapy treatment planning
Bibliographic record
Abstract
17051 Background: Radiation Oncology is a rich and rewarding specialty that requires in-depth understanding of anatomy, physics, dosimetry and oncology. The area of dosimetry is an important and basic fundamental concept to be acquired during residency, and pertinent educational tools are few and far between. The purpose of this project was to develop a self-assessment tool to facilitate radiation therapy treatment planning for non-small cell lung cancer (NSCLC). Methods: We focused our learning module on basic dosimetric and NSCLC treatment planning objectives that radiation oncology residents will encounter during residency. Interactive questions incorporating objective-related concepts were then devised. Treatment planning program templates were developed using the Eclipse software application. Results: A web-based self-directed learning module was developed as the tool's delivery mechanism. The module's questions were divided into the following sections: Chest Anatomy, ICRU Concepts, Beam Modificators, Integrated NSCLC Treatment Plans, Systematic Treatment Planning Approach and Inhomogeneity Corrections. By answering questions via the module, learners receive immediate feedback, corrections and concept-related explanations. A variety of different question techniques were employed, such as multiple choice questions, open-ended questions and associations. Learners are prompted by the system to draw isodoses and volumes on the screen. Other tasks in the module involve identifying structures, setting treatment fields and analyzing dose volume histograms. The website is publicly available in both English and French at: http://www.radiol.umontreal.ca/radio-onco/ . Conclusions: This self-directed learning module is a complementary tool that will help residents gain a solid grasp of NSCLC treatment planning objectives. Other modules covering radiation therapy treatment planning for a variety of anatomical sites, as well as diseases at different stages will eventually be created and added to the website. This educational project aims to facilitate learning and understanding the concepts of treatment planning, according to the different types of cancer. No significant financial relationships to disclose.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.044 |
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".