Recommended Guidelines for Submission, Trimming, Margin Evaluation, and Reporting of Tumor Biopsy Specimens in Veterinary Surgical Pathology
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
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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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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