Prevalence and Molecular Profile of Breast Carcinoma Using Immunohistochemistry Markers in Mexican Women
Bibliographic record
Abstract
BACKGROUND: In Mexico, breast cancer is the leading cause of death by malignant tumors in women aged 20 and older. The World Health Organization estimates that 69% of deaths caused by breast cancer occur in developing countries. Little is known about the prevalence of breast carcinoma in Mexico and its molecular subclassification. METHODS: This retrospective cross-sectional study included patients who underwent a mastectomy (single, radical or lumpectomy) or a breast tumor biopsy (core-needle or excisional) from January 2002 to December 2018. The primary purpose of the study was to determine the prevalence and molecular profile of breast in comprehensive cancer center in Mexico and compare our results with those published in the US. This study was approved by our scientific and bioethical committee. RESULTS: The final analysis included 379 patients. The youngest patient was 23 years old and the oldest patient was 89; the mean age at diagnosis was 54.63 years. Patients of 40 years old or younger accounted for 48 of the cases (12.66%) and those older than 40 accounted for 331 of the cases (87.33%). The molecular subclassification showed luminal A subtype in 139 cases (36.67%), luminal B subtype in 143 cases (37.73%), human epidermal growth factor receptor 2-positive carcinomas in 32 cases (8.44%) and triple-negative carcinomas in 65 cases (17.15%). Diabetes mellitus was present in 43 patients (11.34%), hypertension in 78 patients (20.58%), obesity in 82 patients (21.63%) and 66 patients reported being treated with exogenous hormone therapy (17.41%). CONCLUSIONS: Breast carcinoma occurs at an earlier age in Mexican women compared to women in the US. Hormone-positive tumors were found to be more prevalent in older patients, while high-grade tumors were more frequently identified in younger patients.
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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 | 0.001 |
| 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) | 0.001 | 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".