Cost containment analysis and access to treatment associated with adopting hypofractionated radiation therapy from the Brazilian perspective
Bibliographic record
Abstract
Background: Estimates show that breast cancer, the leading cause of cancer death in females worldwide, will continue to increase in incidence, highlighting the need for increased treatment capacity. While postoperative radiation therapy (RT) is commonly used to reduce recurrence and mortality, research has shown that moderately hypofractionated radiation therapy (HFRT) and 5-fraction HFRT are equally safe and effective and can reduce treatment costs. This study aimed to compare the cost of conventional RT (50Gy/25), moderately HFRT (40.05Gy/15), and 5-fraction HFRT (26Gy/5) for breast cancer patients in Brazil. Methods: The cost of each RT regimen was calculated using the International Atomic Energy Agency's Radiotherapy Cost Estimator Tool. The potential annual savings were then estimated by applying the cost of each regime to the 2020 Brazilian cancer incidence rates. Findings: The average costs per patient for 25 fractions, 15 fractions, and 5 fractions are $2,699.20, $1,711.98, and $929.81, respectively. The annual cost savings associated with treating 70% of patients with 15 fraction HFRT and 30% of patients with 5 fraction HFRT as compared to treating all patients with 25 fraction RT is $72,929,315.40. The estimated annual productivity of 1 LINAC machine for 25 fractions, 15 fractions, and 5 fractions is 338, 647, and 1,310 patients, respectively. Interpretation: The cost analysis revealed decreased patients' costs and potential for increased EBRT access associated with HFRT in the Brazilian perspective. Funding: None.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".