Collapse of Cancer Care Under the Current Conflict in Sudan
Bibliographic record
Abstract
Sudan has been under an armed conflict between the Sudanese Armed Forces and the Rapid Support Forces (RSF) militia since April 15, 2023. The conflict has turned the country into the largest internal displacement humanitarian crisis with 9.05 million internally displaced persons including 2.2 million children younger than 5 years and caused 1.47 million Sudanese to flee the country as refugees. The conflict has had a major destructive impact on the health system, which has incurred targeting with air raids, ground invasion, vandalization, looting of assets and supplies, and killing of doctors, nurses, and other health personnel. Khartoum Oncology Hospital, Sudan's main cancer hub for treatment, diagnostics, and research has become nonfunctional as a result of the conflict. The National Cancer Institute in Wad Medani, the second largest hub, faced a similar fate as the conflict spread to Al-Gezira State. Patients with cancer have been displaced multiple times in Sudan with grave consequences on the continuity of care, worsening of their disease outcomes and palpable negative impacts on children. The oncology workforce in Sudan have themselves been displaced yet are working hard to provide services and care for patients under impossible circumstances. Sudan's doctors in diaspora have rallied to provide support but they face multiple obstacles. As the conflict continues to spread, we call upon the WHO, the United Nations Children's Fund, St Jude Hospital, and all relevant partners to implement an immediate evacuation operation with urgent air lifts of the affected children to continue their cancer care in neighboring countries as was done in Ukraine and Gaza.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".