AAPM WGVRTO report 390: A survey of veterinary radiation oncology equipment and infrastructure in 2022
Bibliographic record
Abstract
Since 2010, there has been little published data on the state of equipment and infrastructure in veterinary radiation oncology clinical practice. These data are important not only to identify the status and use of technology within the veterinary radiation oncology community but also to help identify the extent of medical physics support. The purpose of our study is to report findings from a survey of veterinary radiation oncologists in the USA, Canada, and select centers outside of North America in 2022. A 40-question survey covering topics such as type of radiotherapy equipment, techniques offered, treatment planning systems and dose calculation algorithms, special techniques, board-certified radiation oncologists and residents, and extent of medical physics support was distributed through an online survey tool. Results from 40 veterinary radiation oncology institutions, with equipment explicitly used for veterinary care, suggest that the current state of practice is not dissimilar to what currently exists in human radiation oncology facilities; techniques and technologies commonly employed include flattening filter-free mode megavoltage beams, volumetric arc therapy, daily cone-beam computed tomography, image-guided radiation therapy, and sophisticated dose calculation algorithms. These findings suggest the need for modern radiation oncology acceptance testing, commissioning, and quality assurance programs within the veterinary community. The increase in veterinary radiation oncology residency positions and increasing sophistication of equipment suggests that increased levels of standardized medical physics support would benefit the veterinary radiation oncology community.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".