Achieving optimal cancer outcomes in East Africa through multidisciplinary partnership: a case study of the Kenyan National Retinoblastoma Strategy group
Bibliographic record
Abstract
BACKGROUND: Strategic, interdisciplinary partnerships are essential to addressing the complex drivers of health inequities that result in survival disparities worldwide. Take for example the aggressive early childhood eye cancer retinoblastoma, where survival reaches 97 % in resource-rich countries, but is as low 30 % in some resource-limited nations, where 92 % of the burden lies. This suggests a need for a multifaceted approach to achieve a tangible and sustainable increase in survival. METHODS: We assembled the history the Kenyan National Retinoblastoma Strategy (KNRbS), using information documented in NGO reports, grant applications, news articles, meeting agendas and summaries. We evaluated the KNRbS using the principles found in the guide for transboundary research partnerships developed by the Swiss Commission for Research Partnerships with Developing Countries. RESULTS: A nationally co-ordinated approach drawing input and expertise from multiple disciplines and sectors presented opportunities to optimise cure of children with retinoblastoma. Annual meetings were key to achieving the over 40 major outputs of the group's efforts, related to Awareness, Medical Care, Family Support and Resource Mobilization. Three features were found to be critical to the KNRbS success: multidisciplinarity, consistency and flexibility. CONCLUSION: The KNRbS has achieved a number of key outputs with limited financial investment. As a partnership, the KNRbS meets most of the criteria identified for success. Challenges remain in securing the long-term sustainability of its achievements. Elements of the Kenyan National Retinoblastoma Strategy may be useful to other developing countries struggling with limited survival of retinoblastoma and other cancers or rare diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".