Training for tomorrow: Establishing a worldwide curriculum in online adaptive radiation therapy
Bibliographic record
Abstract
This commentary discusses the implementation of online adaptive radiation therapy (oART) in cancer treatment within the context of current challenges faced by radiation therapy professionals. oART enables modifications to treatment plans based on daily imaging, enhancing target accuracy while minimising harm to surrounding organs. Despite its potential to improve patient outcomes, the application of oART is hindered by notable barriers, particularly in human resources. A global shortage of skilled radiation professionals such as radiation therapists or therapeutic radiographers (RTTs), medical physicists and radiation oncologists, along with training challenges in online adaptive techniques, hinders oART implementation and sustainability. Moreover, geographical disparities limit access to advanced training programs, leaving RTTs and their patients in underserved regions at a disadvantage. There is growing global evidence that RTTs are being successfully trained to lead adaptive fractions in both cone-beam computed tomography and magnetic resonance imaging guided oART. This commentary proposes the notion of standards for a global training curriculum to address barriers and expand RTT capabilities in delivering oART. By leveraging artificial intelligence and fostering interdisciplinary collaboration, the radiation therapy field can enhance efficiency and accuracy in oART. Successful training models from leading institutions illustrate the importance of hands-on experience and ongoing mentorship. A coordinated effort among stakeholders is essential to establish a comprehensive global training framework, ultimately improving patient access to oART and elevating standards of care worldwide.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".