Contemporary management of breast cancer in Nigeria: Insights from an institutional database
Bibliographic record
Abstract
High-quality data are needed to guide interventions aimed at improving breast cancer outcomes in sub-Saharan Africa. We present data from an institutional breast cancer database to create a framework for cancer policy and development in Nigeria. An institutional database was queried for consecutive patients diagnosed with breast cancer between January 2010 and December 2018. Sociodemographic, diagnostic, histopathologic, treatment and outcome variables were analyzed. Of 607 patients, there were 597 females with a mean age of 49.8 ± 12.2 years. Most patients presented with a palpable mass (97%) and advanced disease (80.2% ≥ Stage III). Immunohistochemistry was performed on 21.6% (131/607) of specimens. Forty percent were estrogen receptor positive, 32.8% were positive for HER-2 and 43.5% were triple negative. Surgery was performed on 49.9% (303/607) of patients, while 72% received chemotherapy and 7.9% had radiotherapy. At a median follow-up period of 20.5 months, the overall survival was 43.6% (95% CI -37.7 to 49.5). Among patients with resectable disease, 18.8% (57/303) experienced a recurrence. Survival was significantly better for early-stage disease (I and II) compared to late-stage disease (III or IV) (78.6% vs 33.3%, P < .001). Receipt of adjuvant radiotherapy after systemic chemotherapy was associated with improved survival in patients with locally advanced disease (68.5%, CI -46.3 to 86 vs 51%, CI 38.6 to 61.9, P < .001). This large cohort highlights the dual burden of advanced disease and inadequate access to comprehensive breast cancer care in Nigeria. There is a significant potential for improving outcomes by promoting early diagnosis and facilitating access to multimodality treatment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".