Unseen and unheard: African children with cancer are consistently excluded from clinical trials
Bibliographic record
Abstract
Cancer in Africa has been described as a runaway train as it now kills more Africans than malaria.1 2 There are an estimated 1 million new cases of cancer on the continent every year and this is expected to double by 2030.3 Cancer in Africa is characterised by high mortality and the disparity between mortality rates in Africa and high-income countries (HIC) is most striking for childhood cancers with mortality rates as high as 80% compared with 20% in HICs like the USA and Canada.4 Despite the stark disparity in burden (85% of childhood cancers occur in low and middle-income countries5) and mortality rates of childhood cancers in Africa, access to clinical trials, which are vital for the development of effective and safe therapeutics and treatment, remains unacceptably low for African children. Over the last two decades, clinical trials have played a key role in improving survival rates for children with cancer in HICs.6 For example, in England, patients with cancer enrolled in clinical trials have significantly higher survival rates than similar patients with cancer who are not enrolled in trials.7 The National Comprehensive Cancer Network has stated that clinical trials are the best way to manage patients with cancer.2 8 Unfortunately, this is not possible for most African countries where very poor prognosis has been linked to late presentation, malnutrition, treatment abandonment, lack of proper supportive services and need for drug dose …
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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 | 0.157 | 0.502 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".