Retinoblastoma Research in Africa: A Scoping Review
Bibliographic record
Abstract
Achieving equity in childhood cancer represents a global challenge. In Africa, where retinoblastoma has high mortality and morbidity, strengthening research capacity is crucial in developing clinical guidelines from local evidence. This review identified the scope of retinoblastoma research conducted in Africa. A systematic search identified English-language studies on retinoblastoma in Africa, published between January 1, 2003 and May 15, 2025. Studies were excluded if retinoblastoma was not the primary focus or if Africa was not the main study location. Data collected included journal information, author affiliations, study type, country, purpose, and results. Quantitative findings were summarized with descriptive statistics, while qualitative findings were narratively synthesized. Of the 1546 citations retrieved, 89 met the inclusion criteria. Single-country studies (n = 85) represented 20 of 54 (37%) African countries, while four multi-country studies increased representation to 43 countries (80%). Most studies were clinical observational (55/89, 62%). Of the 89 studies, 49 (55%) were authored solely by researchers in Africa, but studies which included foreign authors tended to be published in journals with higher journal impact factors (p<0.001). The growth of retinoblastoma research in Africa reflects both expanding local research capacity and increased international collaboration. However, limited experimental research and basic science studies point to opportunities to strengthen the local evidence base needed to inform clinical guidelines for improving retinoblastoma outcomes in Africa.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".