Immunohistochemical Profile and Clinical-Pathological Variants of Breast Cancer in Northeastern Mexico
Bibliographic record
Abstract
Background: Breast cancer is a heterogeneous illness, with subtypes of varying etiology. Estrogen Receptor (ER), Progesterone Receptor (PR) and HER2/neu (Human Epidermal Growth Factor Receptor 2) expressions have been identified as predicting factors. Objective: To demonstrate the possible association of the five immunohistochemical (IHC) expression profiles with clinical and histopathological variables of breast cancer in northeastern Mexico. Methodology: In 522 women with breast carcinoma, five IHC profiles were defined [Luminal A, Luminal B, Mixed, HER2/neu and Triple-negative (TN)]. An analysis was done to determine if there were differences between them in relation to the clinical and histopathological variables. Results: The distribution of the histological subtypes was: luminal A (32.97%), TN (27.53%), HER2/neu (19.02%), mixed (13.41%) and luminal B (7.07%). The average age at diagnosis was 53.07 ± 12.08 years, in 90.5% of the patients the size of the tumor was ≥ 2.0 cm, and 40.94% had lymph node involvement. Luminal A subtype had the highest percentage in the postmenopausal state (63.7%, p=0.071). Illness recurred in 21.01% of the patients (n=116), principally with the TN subtype (28.3%, p=0.012). Conclusions: This study detected the characterization of IHC subgroups in patients treated for breast cancer at a reference center for cancer treatment in northeastern Mexico.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".