Cervical cancer in sub-Saharan Africa: a preventable noncommunicable disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it