Recommended Guidelines for the Conduct and Evaluation of Prognostic Studies in Veterinary Oncology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it