Exploring opportunities & pathways for advanced practice radiation therapy roles in the United States
Bibliographic record
Abstract
The evolution of practice of Radiation Therapy in the United States (U.S.) is inevitable. The scope of a radiation therapists role has progressed with advancing technology, implementation of special procedures and patient care requirements. Internationally, Canada, Australia and the United Kingdom have formalized this evolution through the Advanced Practice Radiation Therapist (APRT) role to provide new models of care, to meet growing demands in the practice of Radiation Oncology, to increase efficiency, decrease cost and retain skilled staff (Harnett et al., 2018; Society of Radiographers; Linden et al., 2019; Coleman et al., 2014) [1–4]. Through evidence based practice, the APRT role has proven to provide benefits for multiple stakeholders including service-reconfiguration to reduce wait times, developing and retaining highly skilled radiation therapists, treatment review and most importantly improving patient care within much needed patient cohorts such as the palliative population (Duffton et al., 2019) [5]. The U.S. is no exception to requiring innovative care models and solutions to similar complex, care delivery challenges experienced internationally. The U.S. is experiencing an increase in demand for cancer services and a rapid rate of technological and treatment advancements. Under the current infrastructure, this has impacted the daily tasks of physicians; increasing workload, increasing the complexity of clinical decision making, increasing movement toward site specific subspecialty practice and pushing the scope of radiation therapists informally toward maximization, increased autonomy and a higher level of education and specialized skills (American Society of Clinical Oncology, 2016; ARRT, 2020; Vu et al., 2018) [6–8]. Objective The following reviews the current radiation therapy practice and professional landscape in the United States as it relates to advanced practice, exploring opportunities and challenges under the U.S. health care infrastructure. This broad analysis provides comparison to other countries and disciplines such as the Radiologist Assistant and Nurse Practitioner for potential pathways to establishing the role and describes current needs and value of the expanding scope of RT's practicing in the U.S.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".