Cervical cancer in sub-Saharan Africa: a preventable noncommunicable disease
Bibliographic record
Abstract
INTRODUCTION: Infections caused by high-risk human papillomavirus (HPV) are responsible for 7.7% of cancers in developing countries, mainly cervical cancer. This disease is steadily increasing in sub-Saharan Africa, with more than 75,000 new cases and 50,000 deaths yearly, further increased by HIV infection. Areas covered: The current status of cervical cancer associated with HPV in sub-Saharan Africa has been systematically revised. The main issues discussed here are related to the public health burden of cervical cancer in sub-Saharan Africa and predictions for the coming decades, including molecular epidemiology and determinants of HPV infection in Africa, and promising prevention measures currently being evaluated in Africa. Expert commentary: By the year 2030, cervical cancer will kill more than 443,000 women yearly worldwide, most of them in sub-Saharan Africa. The increase in the incidence of cervical cancer in Africa could counteract the progress made by African women in reducing maternal mortality and longevity. Nevertheless, cervical cancer is a potentially preventable noncommunicable disease, and intervention strategies to eliminate cervical cancer as a public health concern should be urgently implemented.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".