Clinicopathological characteristics of breast cancer patients from Northern Tanzania: common aspects of late stage presentation and triple negative breast cancer
Bibliographic record
Abstract
PURPOSE: Breast cancer (BC) is the second most common cancer among Tanzanian women. Oestrogen (ER), progesterone and human epidermal growth factor receptor 2 play major roles in prognosis and treatment but data for Tanzania are sparse. This study aimed to determine these patterns and histological types, tumour grading and staging of BC patients in northern Tanzania for a better understanding of BC in the Sub-Saharan African (SSA) setting. METHODS: A cross-sectional study recorded newly diagnosed BC cases at Kilimanjaro Christian Medical Centre between October 2018 and March 2019. Receptor status, histological types and grade, clinical stage and socio-demographic were recorded and descriptive and bivariate analyses performed. RESULTS: = 33; 28.4%). One hundred and two (87.9%) patients had invasive ductal carcinoma (IDC), poorly differentiated tumours (60; 51.7%) and clinical stage III disease (62; 53.0%). ER negative tumours were associated with poorly differentiated histological grade (relative risk (RR): 1.34 (0.87-2.07)), tumour size > 5 cm (RR: 1.67 (0.33-8.35)) and IDC (RR: 3.35 (0.56-20.23)). Clinical stages III & IV (odds ratio (OR): 1.64 (0.63-4.24)) were associated with hormone receptor (HR) negative tumours and metastasis (OR: 1.60 (0.68-3.74)) with TNBC. 18% of the patients reported about first-degree relatives with BC. CONCLUSIONS: Most patients presented in advanced stages and TNBC in their menopause. HR negative tumours were associated with poor histological differentiation and IDC. The high percentage of positive family history of BC and the differences in receptor patterns compared to other parts of the world should urge further genetic research on BC in SSA.
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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 | 0.001 | 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".