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 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".