Association of patient tumour characteristics and 5-Year survival in women with breast cancer in Nigeria: A retrospective cohort study
Bibliographic record
Abstract
Objective: The sociodemographic and biological profiles of breast cancer differ globally, especially in women of African descent. In Nigeria, limited studies explore the impact of these breast cancer-related factors on survival. This study evaluated the association between patients' tumour characteristics and survival in breast cancer patients in three regional tertiary cancer treatment centres across Nigeria. Methodology: Data were extracted from patients' case files with a histopathologic diagnosis of breast cancer from January 1, 2005, to January 1, 2019. Patients' sociodemographic and clinical features were presented, and survival probabilities were reported; the Log-rank test was used to determine the association between time to death and categorical variables. Results: The study included 1020 patients across the three centres. The five-year survival probability was 0.65, and the 95% C.I. was 0.60- 0.69. Most patients were between the ages of 30 and 49, and 49% presented with left-sided breast cancer. Patients aged>70 had significantly better survival. Only 40% had Immunohistochemistry (IHC) done, out of which HR+ breast cancer accounted for 43%, while triple-negative breast cancer was 30%. Patients who received Herceptin and had molecular subtyping (immunohistochemistry) done had a higher survival probability. Conclusions: Most breast cancer patients in Nigeria present at earlier ages with advanced disease, and the 5-year survival rates are lower than global rates. Only a few patients have access to optimal breast cancer management. There is an urgent need to provide equitable access to cancer treatment by scaling up the implementation of the strategic framework on breast cancer screening and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".