An update on the management of breast cancer in Africa
Bibliographic record
Abstract
BACKGROUND: There is limited information about the challenges of cancer management and attempts at improving outcomes in Africa. Even though South and North Africa are better resourceds to tackle the burden of breast cancer, similar poor prognostic factors are common to all countries. The five-year overall Survival rate for breast cancer patients does not exceed 60% for any low and middle-income country (LMIC) in Africa. In spite of the gains achieved over the past decade, certain characteristics remain the same such as limited availability of breast conservation therapies, inadequate access to drugs, few oncology specialists and adherence to harmful socio-cultural practices. This review on managing breast cancer in Africa is authored by African oncologists who practice or collaborate in Africa and with hands-on experience with the realities. METHODS: A search was performed via electronic databases from 1999 to 2016. (PubMed/Medline, African Journals Online) for all literature in English or translated into English, covering the terms "breast cancer in Africa and developing countries". One hundred ninety were deemed appropriate. RESULTS: Breast tumors are diagnosed at earlier ages and later stages than in highincome countries. There is a higher prevalence of triple-negative cancers. The limitations of poor nursing care and surgery, inadequate access to radiotherapy, poor availability of basic and modern systemic therapies translate into lower survival rate. Positive strides in breast cancer management in Africa include increased adaptation of treatment guidelines, improved pathology services including immuno-histochemistry, expansion and upgrading of radiotherapy equipment across the continent in addition to more research opportunities. CONCLUSION: This review is an update of the management of breast cancer in Africa, taking a look at the epidemiology, pathology, management resources, outcomes, research and limitations in Africa from the perspective of oncologists with local experience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".