Reirradiation practices of Radiation Therapists (RePoRT) study
Bibliographic record
Abstract
Purpose: Reirradiation for patients with new, recurrent or metastatic tumors is complex and requires intensive collaboration between Radiation Oncologists, Medical Physicists, and Radiation Therapists (RTT). Aside from dosimetry, little has been reported on the role of the RTT in reirradiation. The study characterized the reirradiation patterns-of-practice of RTTs to understand the knowledge and skills being applied in this increasingly important area of cancer care. Materials and Methods: A cross-sectional, survey was conducted of all RTTs practicing in Canada over a 3-month period. The 48-item questionnaire asked RTTs the frequency of performing a range of reirradiation activities, to self-rate their competency levels, and to identify enablers and barriers to reirradiation practice. The survey was distributed by email and data were analyzed with descriptive statistics or thematic analysis for free-text responses. Results: Responses from 214 RTTs revealed frequent and significant involvement in all steps of reirradiation pathway, ranging from pre-treatment imaging and positioning to patient supportive care. There was lower involvement in advanced reirradiation dosimetry techniques, which coincided lower competency self-ratings and knowledge gaps in this area. Access to prior patient records, standardized reirradiation workflows and multi-disciplinary communication were the most common elements reported as important for reirradiation practice. Conclusions: RTT reported frequent and significant involvement in all steps of the reirradiation care pathway. Providing focused education and training for RTTs on reirradiation, coupled with team workflow optimization may enable more effective, safe and streamlined reirradiation care for patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".