Prognostic Value of Histologic Grading for Feline Mammary Carcinoma
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
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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