Stage-adjusted forecasting of radiotherapy demand and outcome benefits across income groups: Estimating survival and local control gains by 2050
Bibliographic record
Abstract
BACKGROUND: Radiotherapy is a vital component of cancer care, yet access is limited. Global estimates often overlook cancer stage variability across countries with different income levels. This study assesses the supply-demand gap for megavoltage radiotherapy machines (MVMs) from 2012 to 2022 and projects the survival and local control benefits achievable by meeting optimal radiotherapy needs by 2050. METHODS: Global cancer data were from GLOBOCAN 2022. A validated, stage-adjusted radiotherapy utilisation model was adapted using available cancer stage data from LMICs for each geographical region. Population-based models estimating local control and survival benefit from radiotherapy were also stage-adjusted. The overall shortfall was calculated as patients not receiving treatment due to limited MVM availability. Corresponding outcome gaps were estimated by multiplying shortfall cases by their respective benefit percentages. RESULTS: Radiotherapy demand increased by 2.4 million cases from 2012 to 2022. Optimising access would yield survival benefits for >860,000 people annually and improved local control for 3.5 million cases annually. Lower-middle-income countries are estimated to derive the highest population-based benefits in local control (10.65 %) and survival (4.94 %). The number of patients missing radiotherapy is projected to reach 7.9 million by 2050, creating a global local control gap of 1.2 million and a survival gap of 500,000 cases per year. CONCLUSION: This study highlights the urgent need for enhanced policies and expanded infrastructure to address radiotherapy disparities, particularly in LMICs, to improve local control and survival outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".