Pediatric Oncology Clinical Trials and Collaborative Research in Africa: Current Landscape and Future Perspectives
Bibliographic record
Abstract
PURPOSE: Adequate clinical services have yet to be established in the majority of African countries, where childhood cancer survival rates vary from 8.1% to 30.3%. The aim of this review is to describe the landscape of pediatric oncology trials in Africa, identify challenges, and offer future opportunities for research collaborations. METHODS: The study includes data from the International Pediatric Oncology Society (SIOP) global mapping survey, meta-research identifying trials in Africa in ClinicalTrials.gov, and a literature overview of publications on the subject of pediatric oncology clinical research supported by expert opinions on the current situation and challenges. RESULTS: The SIOP global mapping survey received responses from 47 of 54 African countries, of which 23 have active clinical research programs. A preliminary search of ClinicalTrials.gov showed that only 105 (12.1%) of 868 African oncology studies included children and adolescents. Of these, 53 (50.5%) were interventional trials according to the registry's classification. The small number of African trials for children and adolescents included palliative care and leukemia trials. In African oncology journals and international pediatric oncology journals, < 1% of the pediatric oncology publications come from Africa. Services and research were strengthened by international collaboration. National studies focused on clinical needs, local challenges, or interventional priorities. Both the literature review and the expert opinions highlight the need to expand clinical research in Africa, despite ongoing regional instability and lack of resources. CONCLUSION: While a low number of pediatric clinical treatment trials are open to African children and adolescents, clinical research of high quality is being done in Africa. Several initiatives are stimulating the development of the research capacity across the continent, which should increase the publication output.
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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 | 0.187 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".