MO‐FG‐BRB‐02: Uniform Access to Radiation Therapy by 2035: Global Task Force on Radiotherapy for Cancer Control
Bibliographic record
Abstract
The global burden of cancer is growing rapidly with an estimated 15 million new cases per year worldwide in 2015, growing to 19 million by 2025 and 24 million by 2035. The largest component of this growth will occur in low‐to‐middle income countries (LMICs). About half of these cases will require radiation treatment. The gap for available cancer treatment, including radiation therapy, between high‐income countries (HICs) and LMICs is enormous. Accurate data and quantitative models to project the needs and the benefits of cancer treatment are a critical first step in closing the large cancer divide between LMICs and HICs. In this context, the Union for International Cancer Control (UICC) has developed a Global Task Force on Radiotherapy for Cancer Control (GTFRCC) with a charge to answer the question as to what it will take to close the gap between what exists today and reasonable access to radiation therapy globally by 2035 and what the potential clinical and economic benefits are for doing this. The Task Force has determined the projections of cancer incidence and the infrastructure required to provide access to radiation therapy globally. Furthermore it has shown that appropriate investment not only yields improved clinical outcomes for millions of patients but that it also provides an overall economic gain throughout all the income settings where this investment is made. This symposium will summarize the facets associated with this global cancer challenge by reviewing the cancer burden, looking at the requirements for radiation therapy, reviewing the benefits of providing such therapy both from a clinical and economic perspective and finally by looking at what approaches can be used to aid in the alleviation of this global cancer challenge. The speakers are world renowned experts in global public health issues (R. Atun), medical physics (D. Jaffray) and radiation oncology (N. Coleman). Learning Objectives: To describe the global cancer challenge and the corresponding potential benefits of addressing this challenge. To describe what radiation therapy infrastructure, in terms of facilities, equipment and personnel, will be required to address this challenge. To describe models of addressing personnel and infrastructure mobilization and capacity building within regions where significant cancer treatment disparities exist.
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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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".