A SURVEY OF VETERINARY RADIATION FACILITIES IN 2010
Bibliographic record
Abstract
A survey of veterinary radiation therapy facilities in the United States, Canada, and Europe was done in 2010, using an online survey tool, to determine the type of equipment available, radiation protocols used, caseload, tumor types irradiated, as well as other details of the practice of veterinary radiation oncology. The results of this survey were compared to a similar survey performed in 2001. A total of 76 facilities were identified including 24 (32%) academic institutions and 52 (68%) private practice external beam radiation therapy facilities. The overall response rate was 51% (39/76 responded). Based on this survey, there is substantial variation among facilities in all aspects ranging from equipment and personnel to radiation protocols and caseloads. American College of Veterinary Radiology boarded radiation oncologists direct 90% of the radiation facilities, which was increased slightly compared to 2001. All facilities surveyed in 2010 had a linear accelerator. More facilities reported having electron capability (79%) compared to the 2001 survey. Eight facilities had a radiation oncology resident, and academic facilities were more likely to have residents. Patient caseload information was available from 28 sites (37% of radiation facilities), and based on the responses 1376 dogs and 352 cats were irradiated in 2010. The most frequently irradiated tumors were soft tissue sarcomas in dogs, and oral squamous cell carcinoma in cats.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".