Effective Global Cancer Care Requires Radiation Therapy: Defining a Path From No Radiotherapy to Radiotherapy of High Quality Globally
Bibliographic record
Abstract
Abstract 46 Background: The increasing global burden of cancer in health disparity regions is now well recognized. To appropriately address the need, the full spectrum of cancer care is required, including cancer control plans, cancer registries, prevention, diagnosis, treatment, multidisciplinary care, and follow-up. Radiation therapy is required for curative treatment, particularly for solid tumors, and for palliation. The Global Task Force for Radiation for Cancer Control, created by the Union for International Cancer Control, demonstrated that radiotherapy is economically beneficial and affordable (Lancet Oncol. 2015 16:1153-86). Recognizing that many low- and middle-income countries have inadequate or no radiation therapy and that there is a need globally for at least 5,000 radiation therapy machines, a well-designed implementation plan is necessary. This includes attention to the potential danger from misuse or mishandling of cobalt-60 and the potential for remote networking to ensure high-quality treatment. Methods: Experts with a broad range of interests and representatives from public and private sectors met at an International Cancer Expert Corps–sponsored workshop on the CERN campus on November 7 and 8, 2016, to consider future options, including innovative technology, a software and systems approach to dealing with the complexity of radiation therapy, and the need for ongoing education and training. Discussions included replacement of cobalt-60 units over time for security and safety reasons. Results: The International Cancer Expert Corps–CERN workshop produced criteria for what could be a newly designed linear accelerator. Three task groups were established to address technology/software solutions, education and training, and economic issues. Conclusion: The workshop report is forthcoming and the task groups will work to define short- and long-term solutions to this major global shortfall. This workshop represents a potential watershed moment for augmenting global cancer care by bringing together expertise and potential investment at the scope needed to address the gap in radiation treatment capacity. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST C. Norman Coleman No relationship to disclose David Pistenmaa No relationship to disclose David Jaffray Consulting or Advisory Role: IBA Mary Gospodarowicz No relationship to disclose Bhadrasain Vikram No relationship to disclose Steve Myers No relationship to disclose Maurizio Vrentenar No relationship to disclose Ugo Amaldi No relationship to disclose Manjit Dosanjh No relationship to disclose
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".