Research on female breast and gynaecologic cancers in the Democratic Republic of Congo: a scoping review
Bibliographic record
Abstract
Abstract Background In the last decades, the incidence of female breast and gynecologic cancers has been increasing in sub-Saharan African countries including in the Democratic Republic of the Congo (DRC). Although DRC has taken some steps in addressing cancer issues, cancer control is not yet efficiently organized. The DRC national strategy for the fight against cervical and female breast cancers stresses that research conducted on female breast and cervical cancers in DRC is not leveraged to support the Ministry of Health. There is a clear and urgent need to identify the type and extend of research conducted in DRC about female breast cancer and cervical cancers to inform the operational cancer research agenda in DRC. Thus, the purpose of this scoping review is to describe current research and identify research gaps related to research on female breast and gynaecologic cancers in DRC Methods A scoping review was conducted through search of several electronic databases. We included peer-reviewed articles and grey literature resources reporting primary or secondary studies about female breast cancer or gynaecologic cancers in DRC. Results A total of 448 articles were retrieved. After screening, 32 articles were retained for review. Twenty-one articles pertained to cervical cancer, 12 to female breast cancer and 2 to vulva cancer. Five articles focused on several cancer types. There was no article retrieved for the other gynaecologic cancers (uterine, Fallopian tube and ovarian cancers). Most of the studies (60%) used a cross-sectional design. The studies were conducted in six provinces. Conclusion This scoping review has demonstrated significant gaps in female breast and gynaecologic cancer research in DRC. The review’s findings support the need for further research in all areas of the continuum of cancer care, the establishment of a clear and adapted research agenda, advocacy and providers’ capacity development.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".