Access to radical and palliative radiotherapy in low- and middle-income countries: challenges, progress, and future directions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Low- and middle-income countries (LMICs) bear over half of the global cancer mortality but have access to only 5% of global radiotherapy resources. As the cancer burden rises and equity in palliative care gains global attention, a focused review on palliative radiotherapy access in LMICs is both timely and necessary. RECENT FINDINGS: Barriers to radiotherapy access in LMICs are multifaceted, including infrastructure gaps, workforce shortages, geographic centralization, high out-of-pocket costs, and systemic underinvestment. Palliative radiotherapy, despite its proven cost-effectiveness and impact on quality of life, is often excluded from national cancer plans and health strategies. Innovative approaches such as hypofractionation, mobile units, AI-assisted planning, and public-private partnerships are emerging to address these gaps. Efforts by the International Atomic Energy Agency and local governments have led to progress in several regions, with case studies from Africa, Asia, and Latin America showing promising results. SUMMARY: Integrating radiotherapy - particularly palliative radiotherapy - into national cancer and palliative care strategies is essential. Sustainable financing, decentralized service models, and context-specific technologies are critical to overcoming current limitations. Bridging this gap is not only a public health necessity but also a moral imperative to reduce suffering and support patients' dignity and societal contributions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".