Recommended Guidelines for the Conduct and Evaluation of Prognostic Studies in Veterinary Oncology
Bibliographic record
Abstract
There is an increasing need for more accurate prognostic and predictive markers in veterinary oncology because of an increasing number of treatment options, the increased financial costs associated with treatment, and the emotional stress experienced by owners in association with the disease and its treatment. Numerous studies have evaluated potential prognostic and predictive markers for veterinary neoplastic diseases, but there are no established guidelines or standards for the conduct and reporting of prognostic studies in veterinary medicine. This lack of standardization has made the evaluation and comparison of studies difficult. Most important, translating these results to clinical applications is problematic. To address this issue, the American College of Veterinary Pathologists' Oncology Committee organized an initiative to establish guidelines for the conduct and reporting of prognostic studies in veterinary oncology. The goal of this initiative is to increase the quality and standardization of veterinary prognostic studies to facilitate independent evaluation, validation, comparison, and implementation of study results. This article represents a consensus statement on the conduct and reporting of prognostic studies in veterinary oncology from veterinary pathologists and oncologists from around the world. These guidelines should be considered a recommendation based on the current state of knowledge in the field, and they will need to be continually reevaluated and revised as the field of veterinary oncology continues to progress. As mentioned, these guidelines were developed through an initiative of the American College of Veterinary Pathologists' Oncology Committee, and they have been reviewed and endorsed by the World Small Animal Veterinary Association.
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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 | 0.170 | 0.383 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".