ACVR and ECVDI consensus statement: Reporting elements for toxicity criteria of the veterinary radiation therapy oncology group v2.0
Bibliographic record
Abstract
The toxicity criteria of the veterinary radiation therapy oncology group (VRTOG) version 2 guidelines are a substantial update to reflect significant advances in radiation oncology over the last three decades. Radiation therapy techniques provide precise and spatially accurate radiation delivery, which facilitates treating tumors in more anatomic locations and incorporating hypofractionated protocols. The purpose of this update is to aid radiation oncology teams in capturing and grading clinically relevant data that impacts the decision-making process in everyday practice and the assessment of clinical trials involving radiation therapy. A dedicated committee initially updated the criteria to include more anatomical sites and grades to characterize a broad spectrum of possible radiation-induced acute and late tissue changes. Through the revision process, which solicited and incorporated feedback from all radiation oncologists within the American College of Veterinary Radiology (ACVR) and specialists outside the ACVR, the authors endeavored to create a grading structure reflective of clinical decision-making in daily radiation oncology. The updated VRTOG v2 toxicity criteria guideline complements the updated Veterinary Cooperative Oncology Group-Common Terminology Criteria for Adverse Events (VCOG-CTCAE v2) guidelines. Because radiation oncology continues to progress rapidly, the VRTOG toxicity criteria should be regularly updated as adverse event data that will be collected following this update further informs the practice of radiation oncology.
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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 | 0.104 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".