The power of integration: radiotherapy and global palliative care
Bibliographic record
Abstract
Radiotherapy (RT) is a powerful tool for the palliation of the symptoms of advanced cancer, although access to it is limited or absent in many low- and middle-income countries (LMICs). There are multiple factors contributing to this, including assumptions about the economic feasibility of RT in LMICs, the logical challenges of building capacity to deliver it in those regions, and the lack of political support to drive change of this kind. It is encouraging that the problem of RT access has begun to be included in the global discourse on cancer control and that palliative care and RT have been incorporated into national cancer control plans in some LMICs. Further, RT twinning programs involving high- and low-resource settings have been established to improve knowledge transfer and exchange. However, without large-scale action, the consequences of limited access to RT in LMICs will become dire. The number of new cancer cases around the world is expected to double by 2030, with twice as many deaths occurring in LMICs as in high-income countries (HICs). A sustained and coordinated effort involving research, education, and advocacy is required to engage global institutions, universities, health care providers, policymakers, and private industry in the urgent need to build RT capacity and delivery in LMICs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".