Cancer prevention in Africa: a review of the literature
Bibliographic record
Abstract
Cancer is an emerging crisis in Africa. Cancer was the seventh leading cause of death in 2004. If not controlled, cancer incidence in Africa is expected to reach 1.28 million cases annually and claim 970,000 lives yearly by 2030. This paper presents a review of the literature on current cancer prevention approaches in Africa, and consists of cancer prevention studies conducted in African countries (e.g. South Africa and Nigeria) from PubMed, Scopus, and CINAHL databases. Common female cancers in Africa are breast and cervical cancer while prostate cancer is the most common neoplasm among African males. Other common cancers are liver, colorectal, and non-Hodgkin's lymphoma. Mortality related to these cancers comes as a result of delays in screening and treatment, unfamiliarity with cancer and cancer prevention, inaccessibility and unaffordability of care, and inefficiency of healthcare systems. Cancer prevention efforts are deficient because many governments lack cancer prevention and control policies. Also contributing to the lack of cancer prevention and control policies are low levels of awareness, scarce human and financial resources, and inadequacy of cancer registries. Overall, governments grapple with limited funds and competing healthcare priorities. As cancer continues to increase in Africa, the need for rigorous interdisciplinary research on cancer etiology and monitoring in Africa has never been timelier. Cost-effective cancer prevention programs, coordination of donor funding, advocacy, and education should be aggressively pursued. The call for more collaborative approaches in research and policy is urgently needed.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".