Distribution of Breast Cancer Subtypes Among Nigerian Women and Correlation to the Risk Factors and Clinicopathological Characteristics
Bibliographic record
Abstract
BACKGROUND: Breast cancer in African women differs from the Caucasian. Understanding the profile of Nigerian women with breast cancer will help with preventive measures and treatment. This study focused on the clinico-pathological characteristics, with risk factors of breast cancer patients in Nigeria. METHODS: Newly diagnosed female patients with breast cancer were assessed over 12 months. Patients were reviewed using a predesigned proforma which focused on socio-demographic information, clinical information, risk factors and tumor biology. RESULTS: A total of 251 women were identified; their mean age was 46 years. More than half (62.5%) are premenopausal at presentation, 37.8% with Eastern Cooperative Oncology Group (ECOG) score of 0 and right side (50.2%) as the most common primary site of disease. Less than half of them (43.0%) are estrogen receptor (ER) positive, 27.9% are progesterone receptor (PR) positive, 43.8% and 47.4% are hormone receptor positive and triple negative, respectively. Most patients presented at the latter stage of the disease, stage III (66.9%) and stage IV (18.3%). Only 15.9% are well differentiated and almost all (92.8%) had invasive ductal histological type. Obesity (66.2%) and physical inactivity (41.9%) are the most common risk factors for the disease. A significant relationship was found between immunohistochemistry status and family history of breast cancer, tumor site, previous breast surgery, previous lump and alcohol intake. CONCLUSION: Findings from this study showed that Nigerian breast cancer patients differ from their counterparts in the high human development index (H-HDI) countries in terms of the patients and disease characteristics. In view of this, prevention and treatment options should consider this uniqueness to ensure better outcome.
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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) | 0.002 | 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".