A roadmap to establishing global oncology as a priority initiative within a National Cancer Institute–designated cancer center
Bibliographic record
Abstract
As the burden of cancers impacting low- and middle-income countries is projected to increase, formation of strategic partnerships between institutions in high-income countries and low- and middle-income country institutions may serve to accelerate cancer research, clinical care, and training. As the US National Cancer Institute and its Center for Global Health continue to encourage cancer centers to join its global mission, academic cancer centers in the United States have increased their global activities. In 2015, the Helen Diller Family Comprehensive Cancer Center at the University of California, San Francisco, responded to the call for international partnership in addressing the global cancer burden through the establishment of the Global Cancer Program as a priority initiative. In developing the Global Cancer Program, we galvanized institutional support to foster sustained, bidirectional, equitable, international partnerships in global cancer control. Our focus and intent in disseminating this commentary is to share experiences and lessons learned from the perspective of a US-based, National Cancer Institute-designated cancer center and to provide a roadmap for other high-income institutions seeking to strategically broaden their missions and address the complex challenges of global cancer control. Herein, we review the formative evaluation, governance, strategic planning, investments in career development, funding sources, program evaluation, and lessons learned. Reflecting on the evolution of our program during the first 5 years, we observed in our partners a powerful shift toward a locally driven priority setting, reduced dependency, and an increased commitment to research as a path to improve cancer outcomes in resource-constrained settings.
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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 | 0.116 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.015 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 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".