Po‐Thur Eve General‐38: The Virtual Radiotherapy Department
Bibliographic record
Abstract
With the number of clinical cases increasing each year, there is pressure on all clinical staff to do more clinical service. At the same time there is a demand to train more students in all of the radiotherapy disciplines. Regulatory agencies are also demanding more continuing education and upgrading for existing staff. We have recently introduced a new clinical treatment planning system, which requires a significant amount of training for all staff members. To meet these increased demands for training all levels of learners we have developed a “Virtual Radiotherapy Department”. The Virtual Radiotherapy Department will provide on line teaching of basic physics and treatment planning for radiation therapy students, radiation oncology residents, and physics graduate students and residents. By using on‐line interactive tools it is possible to teach all the different students at different levels by making basic information available to all students, but linking to more advanced information for those that require it. On‐line videos have been created describing several clinical techniques using various equipment. These videos will be used to provide students instruction as well as for continuing education for existing staff or new staff. These tools will be used to provide training to all users as we introduce our new treatment planning system, showing them exactly how to complete various tasks. These videos are customized to our treatment processes; therefore, they show users exactly what is required in the most efficient manner. Funding Provided by Alberta Health and Wellness and Varian.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.045 |
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".