Survival status of women with cervical cancer in Sub-Saharan Africa: a systematic review and meta-analysis, 2024
Bibliographic record
Abstract
Background: Despite the availability of vaccination and early treatment, cervical cancer remains a significant public health concern globally, particularly in Sub-Saharan Africa, where access to screening and treatment is often limited. Methods: In this study, researchers conducted a survey of four international databases-Medline/PubMed, Scopus, Web of Science, and Henare-along with Google Scholar to search for gray literature. The keywords used for searching the international databases included "Uterine Cervical Neoplasms [Mesh]," "Survival OR Survival Analysis OR Survival Rate," and "Sub-Saharan countries" (including the names of specific countries). Six researchers independently screened and extracted data from the articles. All studies published in English were included without restriction and assessed for quality using the adapted Newcastle-Ottawa Scale for cohort and cross-sectional studies. The results of this systematic review were reported in accordance with the PRISMA checklist. Results: Out of the 2,180 articles initially identified, 23 were deemed eligible and reported on the survival status of patients with cervical cancer in Sub-Saharan Africa. This study assessed the multi-year survival rates (1, 2, 3, 4, and 5 years) of patients with cervical cancer. Based on the random-effects model, the overall pooled 1-year survival was 65.0% [95% confidence interval (CI), 52-78] with I² = 99.31 and p-value < 0.001. The 2-year survival rate was 60% (95% CI, 46-74) with I² = 99.12 and p-value < 0.001, the 3-year survival was 48% (95% CI, 35-62) with I² = 98.45 and p-value < 0.001, the 4-year survival was 42.9% (95% CI, 32.7-53.1) with I² = 96.80 and p-value < 0.001, and the 5-year survival was 35% (95% CI, 27-44) with I² = 98.74 and p-value < 0.001. Conclusions: This systematic review and meta-analysis found that the survival rates for patients with cervical cancer in Sub-Saharan Africa are much lower than the global averages. The results show that the 5-year survival rate can be as low as 35%, highlighting serious challenges in managing cervical cancer in this region. To address this issue, collaboration among governments, healthcare providers, and international organizations is essential to enhance the availability and quality of care. Future research should focus on developing effective early detection and treatment strategies and monitoring long-term survival outcomes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".