Radiotherapy utilization in developing countries: An IAEA study
Bibliographic record
Abstract
Background The planning of national radiotherapy (RT) services requires a thorough knowledge of the country's cancer epidemiology profile, the radiotherapy utilization (RTU) rates and a future projection of these data. Previous studies have established RTU rates in high-income countries. Methods Optimal RTU (oRTU) rates were determined for nine middle-income countries, following the epidemiological evidence-based method. The actual RTU (aRTU) rates were calculated dividing the total number of new notifiable cancer patients treated with radiotherapy in 2012 by the total number of cancer patients diagnosed in the same year in each country. An analysis of the characteristics of patients and treatments in a series of 300 consecutive radiotherapy patients shed light on the particular patient and treatments profile in the participating countries. Results The median oRTU rate for the group of nine countries was 52% (47–56%). The median aRTU rate for the nine countries was 28% (9–46%). These results show that the real proportion of cancer patients receiving RT is lower than the optimal RTU with a rate difference between 10–42.7%. The median percent-unmet need was 47% (18–82.3%). Conclusions The optimal RTU rate in middle-income countries did not differ significantly from that previously found in high-income countries. The actual RTU rates were consistently lower than the optimal, in particular in countries with limited resources and a large population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".