[Histo-Molecular Profiles Of Male Breast Cancers In Mali].
Bibliographic record
Abstract
INTRODUCTION: the occurrence of breast cancer in men is an indication for genetic counseling and must be systematically sought. The objective of this work was to describe the histopathological and molecular aspects of human breast cancer. MATERIAL AND METHODS: This was a cross-sectional study with retrospective collection from January 2007 to September 2021, including all cases of male breast cancer diagnosed by histology with immunohistochemical examination. The variables studied were histopathological and molecular. The samples were fixed in 10% formalin and processed using the standard hematoxylin-eosin and immunohistochemistry technique. RESULTS: A total of 8 cases of male breast cancer were collected. Each case underwent histological and immunohistochemical examination. Age ranged from 8 to 77 years with a mean age of 50.63±20.79. Consultation time of less than 12 months was common with the presence of a breast nodule as the reason. The left breast was as affected as the right breast. Tumor size T3 (5 cm and more) predominated with 3 cases. The nature of the sample received was: 04 biopsies and 04 lumpectomies. Infiltrative carcinoma of non-specific type was common: 7 cases (or 87.5%) with Elston and Ellis grade II in 75% of cases. Luminal A and triple negative were predominant with 3 cases each (37.5%). CONCLUSION: Breast cancer is increasingly diagnosed in men at all ages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".