Robotic radiosurgery for the treatment of one to three brain metastases: A pragmatic application of cost-benefit analysis using willingness to pay.
Bibliographic record
Abstract
17 Background: With the emergence of radiosurgery as a new radiotheraputic 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. Methods: 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. Results: The yearly incremental cost of providing robotic radiosurgery was $99,177. The mean community yearly willingness-to pay for robotic radiosurgery was $2,300,000, p=0.03. The calculated yearly net societal benefit for robotic radiosurgery was $2,200,823. Among participants who preferred robotic radiosurgery there was no evidence of starting point bias, p=0.8. Conclusions: 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.
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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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".