Growing the global cancer care system: success stories from around the world and lessons for the future
Bibliographic record
Abstract
Despite major biomedical advancements in various realms of oncology, the benefits of these developments are not equitably distributed, particularly in underresourced settings. Although much work has described the challenges and systemic barriers in global cancer control, in this article we focus on success stories. This article describes clinical care delivered at Rwanda's Butaro Cancer Center of Excellence, the cancer research collaborations under India's National Cancer Grid, and the efforts of Latin America's Institute of Cancer of São Paulo in advancing cancer care and training. These examples highlight the potential of strategic collaborations and resource allocation strategies in improving cancer care globally. We emphasize the critical role of partnerships between physicians and allied health professionals, funders, and policy makers in enhancing access to treatment and infrastructure, advancing contextualized research and national guidelines, and establishing regional and global collaborations. We also draw attention to challenges faced in diverse global settings and outline benchmarks to measure success in the fight against cancer.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".