Three Discipline Collaborative Radiation Therapy (3DCRT) special debate: Radiation oncology has become so technologically complex that basic fundamental physics should no longer be included in the modern curriculum for radiation oncology residents
Bibliographic record
Abstract
Radiation Oncology is a highly multidisciplinary medical specialty, drawing significantly from three scientific disciplines-medicine, physics, and biology.As a result, discussion of controversies or changes in practice within radiation oncology involves input from all three disciplines.As a result, we have adopted this "team-science" approach to the traditional debates featured in this journal.This article is part of a series of special debates entitled "Three Discipline Collaborative Radiation Therapy (3DCRT)" in which each debate team has included three multidisciplinary team members, with the hope that this format would be both engaging for the readership and foster further collaboration in the science and clinical practice of radiation oncology.Previous 3DCRT debates have included a radiation oncologist, medical physicist, and radiobiologist on each team.For this debate, we break that trend and include a seasoned radiation oncologist, an early career radiation oncologist, and a medical physicist on each team.We hope these perspectives add valuable insight to this particular debate. INTRODUCTIONPhysics is one of the fundamental scientific pillars of radiation oncology.Its principles form the foundation for everything from the creation of the radiation we use, to how it interacts in the patient, to how we create and deliver our treatments.As such, it represents one of the core didactic elements of radiation oncology residency training.However, radiation oncology has undergone a staggering increase in technological complexity over the
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.013 | 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".