Veterinary <scp>Cooperative</scp> Oncology Group—Common Terminology Criteria for Adverse Events (<scp>VCOG‐CTCAE</scp> v2) following investigational therapy in dogs and cats
Bibliographic record
Abstract
The updated VCOG-CTCAE v2 guidelines contain several important updates and additions since the last update (v1.1) was released in 2011 and published within Veterinary and Comparative Oncology in 2016. As the Veterinary Cooperative Oncology Group (VCOG) is no longer an active entity, the original authors and contributors to the VCOG-CTCAE v1.0 and v1.1 were consulted for input, and additional co-authors sought for expansion and refinement of the adverse event (AE) categories. VCOG-CTCAE v2 includes expanded neurology, cardiac and immunologic AE sections, and the addition of procedural-specific AEs. It is our intent that, through inclusion of additional authors from ACVIM subspecialties and the American College of Veterinary Surgery, that we can more comprehensively capture AEs that are observed during clinical studies conducted across a variety of disease states, clinical scenarios, and body systems. It is also our intent that these updated veterinary CTCAE guidelines will offer improved application and ease of use within veterinary practice in general, as well as within clinical trials that assess new therapeutic strategies for animals with a variety of diseases. Throughout the revision process, we strived to ensure the grading structure for each AE category was reflective of the decision-making process applied to determination of dose-limiting events. As phase I trial decisions are based on these criteria and ultimately determine the maximally tolerated dose, there is impact on standard dosing recommendations for any new drug registration or application. This document should be updated regularly to reflect ongoing application to clinical studies carried out in veterinary 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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".