THE IMPLICATION OF GENETIC MEDICINE IN BREAST CANCER THERAPY IN NIGERIA: CLINICAL PRACTICE AND RESEARCH
Bibliographic record
Abstract
In the preceding three decades, breast cancer occurrence and mortality rates have proliferated in Nigeria. Despite the considerable health, socioeconomic and developmental burdens breast cancer imposes on Nigeria, researchers have not extensively explored the use of genetic medicine in the management of this disease in Nigerian patients. This review’s objectives were to compare the diagnosis, treatment, and research of breast cancer in Nigeria and other countries. In addition, it also highlighted the setbacks and difficulties in breast cancer management in Nigeria. This journal employs a literature review. Detailed relevant articles were researched in two main electronic databases - Google Scholar and PubMed. The databases were analysed for keywords including: “breast cancer,” “breast cancer therapy,” “breast cancer diagnosis,” “breast cancer in Nigeria,” and “genetic medicine in breast cancer.” Only journals written in the English language between 1998 and 2022 were considered. 34 journals were identified, of which 22 were used for this review. Findings showed that genetics is not often considered for predicting and treating breast cancer. They also show that due to late presentation at the hospital, triple-negative breast cancer, usually at stage III or IV, is the most common breast cancer type in Nigeria. Genetic medicine should be integrated into the therapy and management of breast cancer in Nigeria. It will allow prediction of the disease, and timely diagnosis and ultimately possibly lead to a decline in breast cancer mortality and morbidity, just like in developed countries (high-income countries) such as The United States of America, Canada, and Sweden.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".