High Incidence of Triple-Negative Tumors in Sub-Saharan Africa: A Prospective Study of Breast Cancer Characteristics and Risk Factors in Malian Women Seen in a Bamako University Hospital
Bibliographic record
Abstract
OBJECTIVE: Few studies have been conducted on breast cancer in Sub-Saharan Africa and their results have been suspected to be impaired by artefacts. This prospective study was designed to determine tumor and patient characteristics in Mali with control of each methodological step. These data are necessary to define breast cancer treatment guidelines in this country. METHODS: Clinical and tumor characteristics and known risk factors were obtained in a consecutive series of 114 patients. Each technical step for the determination of tumor characteristics [histology, TNM, grade, estrogen (ER) and progesterone receptors (PR), HER2, and Ki67] was controlled. RESULTS: Patients had a mean age of 46 years. Most tumors were invasive ductal carcinomas (94%), T3-T4 (90%) with positive nodes (91%), grade III (78%), and ER (61%) and PR (72%) negative. HER2 was overexpressed in 18% of cases. The triple-negative subgroup represented 46%, displaying a particularly aggressive pattern (90% grade III; 88% Ki67 >20%). CONCLUSION: This study demonstrates the high incidence of aggressive triple-negative tumors in Mali. Apart from a higher prevalence of premenopausal women, no significant difference in risk factors was observed between triple-negative tumors and other tumors. The hormonal therapy systematically prescribed therefore needs to be revised in light of this study.
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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".