Robotic Radiosurgery for the Treatment of 1–3 Brain Metastases: A Pragmatic Application of Cost-Benefit Analysis Using Willingness-To-Pay
Bibliographic record
Abstract
With the emergence of radiosurgery as a new radiotherapeutic technique, health care decision makers are required to incorporate community need, cost and patient preferences when allocating radiosurgery resources. Conventional patient utility measures would not reflect short term preferences and would therefore not inform decision makers when allocating radiosurgery treatment units. The goal of this article is to demonstrate the feasibility of cost-benefit analysis to elicit the yearly net monetary benefit of robotic radiosurgery. To calculate the yearly incremental cost of robotic radiosurgery as compared to fixed gantry radiosurgery we used direct local cost data. We assumed a standard 10 year replacement and 5% amortization rate. Decision boards summarizing the clinical scenario of brain metastases and the difference between robotic and fixed gantry radiosurgery in terms of immobilization, comfort and treatment time were then presented to a sample of 18 participants. Participants who preferred robotic radiosurgery were randomly assigned to either a low ($1) or high ($5) starting point taxation based willingness-to-pay algorithm. The yearly incremental cost of providing robotic radiosurgery was $99,177 CAD. The mean community yearly willingness-to-pay for robotic radiosurgery was $2,300,000 CAD, p = 0.03. The calculated yearly net societal benefit for robotic radiosurgery was $2,200,823 CAD. Among participants who preferred robotic radiosurgery there was no evidence of starting point bias, p = 0.8. We have shown through this pilot study that it is feasible to perform cost-benefit analysis to evaluate new technologies in Radiation Oncology. Cost-benefit analysis offers an analytic method to evaluate local preferences and provide accountability when allocating limited healthcare resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".