State of Cancer Control in Rwanda: Past, Present, and Future Opportunities
Bibliographic record
Abstract
Rwanda is a densely populated low-income country in East Africa. Previously considered a failed state after the genocide against the Tutsi in 1994, Rwanda has seen remarkable growth over the past 2 decades. Health care in Rwanda is predominantly delivered through public hospitals and is emerging in the private sector. More than 80% of patients are covered by community-based health insurance (Mutuelle de Santé). The cancer unit at the Rwanda Biomedical Center (a branch of the Ministry of Health) is responsible for setting and implementing cancer care policy. Rwanda has made progress with human papillomavirus (HPV) and hepatitis B vaccination. Recently, the cancer unit at the Rwanda Biomedical Center launched the country's 5-year National Cancer Control Plan. Over the past decade, patients with cancer have been able to receive chemotherapy at Butaro Cancer Center, and recently, the Rwanda Cancer Center was launched with 2 linear accelerator radiotherapy machines, which greatly reduced the number of referrals for treatment abroad. Palliative care services are increasing in Rwanda. A cancer registry has now been strengthened, and more clinicians are becoming active in cancer research. Despite these advances, there is still substantial work to be done and there are many outstanding challenges, including the need to build capacity in cancer awareness among the general population (and shift toward earlier diagnosis), cancer care workforce (more in-country training programs are needed), and research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".