Analysis of Global Radiotherapy Needs and Costs by Geographic Region and Income Level
Bibliographic record
Abstract
Recent years have seen various reviews on the lack of access to radiotherapy often based on geographic regions of the world such as Africa, Asia Pacific, Europe, Latin America and North America. Countries are often defined by their national income per capita levels based on World Bank definitions of high income, upper middle income, lower middle income and low income. Within the world regions, there are significant variations in gross national income (GNI) per capita among the different countries, and even within similar income levels, large variations exist. This report presents the actual status of radiotherapy and analyses the current needs and costs to provide full access in the different regions of the world. Actual coverage of the needs ranges from 34% in Africa to over 92% in Europe to about double the needs in North America. In line with this, proportional additional investments and operational costs are as high as more than 200% in Africa to almost none in North America. Two world regions face substantial challenges: Africa, based on the important demands to build new capacity and subsequently to maintain operational capability; and Asia Pacific, due to its high population density, translating into large absolute needs in radiotherapy treatments and resources, and hence in associated costs. With the data highlighting a large variability of GNI/capita even within similar income levels in the various world regions, it is expected that additional investment in resources and costs may be more dependent on income level of the country than on the GNI group or the geographic region of the world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".