Recommended Guidelines for Submission, Trimming, Margin Evaluation, and Reporting of Tumor Biopsy Specimens in Veterinary Surgical Pathology
Bibliographic record
Abstract
Neoplastic diseases are typically diagnosed by biopsy and histopathological evaluation. The pathology report is key in determining prognosis, therapeutic decisions, and overall case management and therefore requires diagnostic accuracy, completeness, and clarity. Successful management relies on collaboration between clinical veterinarians, oncologists, and pathologists. To date there has been no standardized approach or guideline for the submission, trimming, margin evaluation, or reporting of neoplastic biopsy specimens in veterinary medicine. To address this issue, a committee consisting of veterinary pathologists and oncologists was established under the auspices of the American College of Veterinary Pathologists Oncology Committee. These consensus guidelines were subsequently reviewed and endorsed by a large international group of veterinary pathologists. These recommended guidelines are not mandated but rather exist to help clinicians and veterinary pathologists optimally handle neoplastic biopsy samples. Many of these guidelines represent the collective experience of the committee members and consensus group when assessing neoplastic lesions from veterinary patients but have not met the rigors of definitive scientific study and investigation. These questions of technique, analysis, and evaluation should be put through formal scrutiny in rigorous clinical studies in the near future so that more definitive guidelines can be derived.
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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 | 0.076 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".