Cost Minimization Analysis of Hypofractionated Radiotherapy
Bibliographic record
Abstract
Early-stage breast cancer patients comprise a large proportion of patients treated with radiotherapy in Canada. Proponents have suggested that five-fraction hypofractionated radiotherapy for these patients would result in significant cost savings. An assessment of this argument is thus warranted. The FAST-Forward and UK FAST clinical trials each demonstrated that their respective hypofractionated regimens provided equivalent outcomes compared with standard regimens. Thus, a cost-minimization analysis was performed to quantify the potential savings associated with these regimens, which were designated as FAST-Forward 1 (26 Gy/5 fractions/1 week) and FAST-Forward 2 (27 Gy/5 fractions/1 week), and UK FAST 1 (28.5 Gy/5 fractions/5 weeks) and UK FAST 2 (30 Gy/5 fractions/5 weeks). A standard regimen of 42.5 Gy/16 fractions/5 weeks was also included. A comprehensive model of radiotherapy costs for a Canadian cancer centre was created. Time, labour costs, and capital costs were calculated for each regimen and applied using established measures. The total costs per patient for the FAST-Forward trials were $851.77 for FAST-Forward 1 and $874.77 for FAST-Forward 2, providing a total savings of $487.99 and $464.99, respectively. Similarly, the total costs per patient for the FAST trials were $979.75 for UK FAST 1 and $1017.70 for UK FAST 2, providing savings of $360.01 and $322.06, respectively. Following the FAST-Forward 1 regimen results in the greatest reduction of infrastructure and human resources costs at 36.42% compared with the standard. Sensitivity analysis shows a maximum per-patient costs savings ranging from $474.60 to $508.53 for the FAST-Forward 1 trial, which translates to an annual savings of $174,700/year locally and $2.06 million/year province-wide, based on a moderate-to-large size department workload. Compared with a standard radiotherapy regimen, all FAST-Forward and UK FAST hypofractionated regimens provide cost savings for the treatment of early-stage breast cancer. The cost savings associated with each of these equivalent regimens can be directly calculated; activities in this model can easily be adjusted to account for cost variations, allowing other centres to calculate cost impacts specific to their own centres.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".