Le neoplasie mammarie della cagna: valutazione clinica dei fattori di rischio e prognostici
Bibliographic record
Abstract
The aim of the present study was to evaluate the role of different risk and prognostic factors of mammary carcinoma in bitches. Bitches enrroled in the study were submitted to a two years follow up: relapse, new neoplasia, metastasis and death were taken into consideration. Different aspects were considered. In our population mammary gland tumors occurred mostly in 9.4 (mean) year old bitches with a higher incidence between 10 and 11 years (range 4 to 15 years). The study showed an increased risk of mammary gland tumors only in Dalmatian and Yorkshire Terrier dogs but not in other breeds, different from other previous studies . A significant reduction in mammary neoplasia incidence was found in Labrador and Golden Retriever bitches. Both obesity and home-made diet may be an important risk factors. No significant correlation between diet tipology, body size and body condition score was found. Suburban environment seemed to play an important role in developing of mammary gland tumors. The topographic distribution of neoplasias was in agreement with previous studies showing a higher incidence of tumors in inguinal mammary glands (more than 60%). A statistically significant correlation (p<0.05) was found between the reproductive phase cycle at the moment of the diagnosis of mammary tumors their localization. Moreover bitches in cycling activity (proestrus, estrus and diestrus) showed a higher probability to develop inguinal mammary tumors. While the mammary tumors arosen during the cycling activity had only an inguinal localization, during anaestrus they developed in all mammary glands, showing a major incidence in caudal glands. No statistically significant correlation was found between the anamnestic report of pseudopregnancy and the percentage of malignancy, pseudopregnancy and multiple tumors, and between pseudopregnancy and neoplastic localization. Ovariectomy didn’t influence significantly the relapse and the metastasis of mammary carcinoma and the surviving of bitches 6, 12 and 24 months after surgery. On the contrary, ovariectomy showed a significant influence (p<0.05) on the risk of developing new tumors after surgery. TNM in the first two stages seemed to be inadequate for giving an accurate prognosis and assessing a therapeutic plan. Histotype, histologic grading and histologic staging seemed to be adequate and accurate prognosis factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".