The International Cancer Expert Corps: A Unique Approach for Sustainable Cancer Care in Low and Lower-Middle Income Countries
Bibliographic record
Abstract
The growing burden of non-communicable diseases including cancer in low- and lower-middle income countries (LMICs) and in geographic-access limited settings within resource-rich countries requires effective and sustainable solutions. The International Cancer Expert Corps (ICEC) is pioneering a novel global mentorship-partnership model to address workforce capability and capacity within cancer disparities regions built on the requirement for local investment in personnel and infrastructure. Radiation oncology will be a key component given its efficacy for cure even for the advanced stages of disease often encountered and for palliation. The goal for an ICEC Center within these health disparities settings is to develop and retain a high-quality sustainable workforce who can provide the best possible cancer care, conduct research, and become a regional center of excellence. The ICEC Center can also serve as a focal point for economic, social, and healthcare system improvement. ICEC is establishing teams of Experts with expertise to mentor in the broad range of subjects required to establish and sustain cancer care programs. The Hubs are cancer centers or other groups and professional societies in resource-rich settings that will comprise the global infrastructure coordinated by ICEC Central. A transformational tenet of ICEC is that altruistic, human-service activity should be an integral part of a healthcare career. To achieve a critical mass of mentors ICEC is working with three groups: academia, private practice, and senior mentors/retirees. While in-kind support will be important, ICEC seeks support for the career time dedicated to this activity through grants, government support, industry, and philanthropy. Providing care for people with cancer in LMICs has been a recalcitrant problem. The alarming increase in the global burden of cancer in LMICs underscores the urgency and makes this an opportune time fornovel and sustainable solutions to transform cancer care globally.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".