Women’s cancers in Sudan with a focus on cervical cancer: turmoil, geopolitics and opportunities
Bibliographic record
Abstract
Cancer is the leading cause of death worldwide and the second leading cause of death in Sudanese women. However, despite proven interventions for primary, secondary and tertiary prevention and the World Health Organization's call to action toward eliminating cervical cancer, there has been little progress in addressing the cervical cancer burden in Sudan. This short communication intends to shed light on the challenges facing women's cancers in Sudan, taking cervical cancer as an example. It also discusses the opportunities and suggests ways to improve the outcomes of women's cancers in Sudan. Sudan's government should urgently implement a broad public health strategy to improve outcomes for women with cancer. The cancer control plan should be aligned with international, evidence-based recommendations and adapted to local circumstances. It should strengthen health literacy, augment different health care interventions, including vaccination, committed screening programmes, early detection and proper diagnosis of symptomatic cases, a programmatic approach to active management and palliative care and ensure robust referral pathways. Policies are also needed in collaboration with the international community in addressing the cancer care needs of internally displaced and refugee women in Sudan. The strategy should consider overcoming the existing challenges and making the most opportunities available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".