Prognostic Value of Histologic Grading for Feline Mammary Carcinoma
Bibliographic record
Abstract
Feline mammary carcinoma is highly malignant and generally associated with a poor prognosis, although studies suggest the range of survival times in affected cats is broad. Histologic grading of these tumors is achieved using the Elston and Ellis system, originally developed for human breast cancer. In cats, however, classification using this method has variable prognostic value. Therefore, objectives of this study were (1) to evaluate the Elston and Ellis grading system for feline mammary carcinoma in a predominantly spayed population and (2) to determine whether modification of this system or development of a novel system improved the prognostic value of histologic grading. Survey data and histologic features for 108 carcinomas from 97 cats were analyzed with respect to overall survival. Elston and Ellis grading failed to correlate significantly with overall survival. Using multivariable analysis, lymphovascular invasion, nuclear form, and mitotic count each demonstrated independent prognostic significance (P = .008, <.001, and .004, respectively). Modifications of the Elston and Ellis system and a novel grading system were proposed based on these results; all showed significant correlation with overall survival (P < .001). Median survival times were 27, 29, or 31 months for grade I; 14, 12, or 14 months for grade II; and 13, 5, or 8 months for grade III carcinomas using the mitotic-modified Elston and Ellis, the revised Elston and Ellis, or the novel grading system, respectively. Based on this retrospective study, adoption of the species-specific systems as proposed here may improve the prognostic value of histologic grading for feline mammary carcinoma.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".