Cost-effectiveness analysis of MR-guided focused ultrasound thalamotomy for tremor-dominant Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVE: The development of transcranial MR-guided focused ultrasound (MRgFUS) has revitalized the practice of lesioning procedures in functional neurosurgery. Previous health economic analysis found MRgFUS thalamotomy to be a cost-effective treatment for patients with essential tremor, supporting its reimbursement. With the publication of level I evidence in support of MRgFUS thalamotomy for patients with tremor-dominant Parkinson's disease (TDPD), the authors performed a health economic comparison between MRgFUS, deep brain stimulation (DBS), and medical therapy. METHODS: The authors used a decision tree model with rollback analysis and one-factor sensitivity analysis. Literature searches of MRgFUS thalamotomy and unilateral DBS of the ventrointermediate nucleus of the thalamus for TDPD were performed to determine the utility and probabilities for the model. Costs in Canadian dollars (CAD) were derived from the Schedule of Benefits and Fees in Ontario, Canada, and expert opinion on usage. RESULTS: MRgFUS was associated with an expected cost of $14,831 CAD. Adding MRgFUS to continued medical therapy resulted in an incremental cost-effectiveness ratio of $30,078 per quality-adjusted life year (QALY), which remained cost-effective under various scenarios in the sensitivity analysis. Comparing DBS to MRgFUS, while DBS did not achieve the willingness-to-pay threshold ($56,503 per QALY) in the base case scenario, it did so under several scenarios in the sensitivity analysis. CONCLUSIONS: MRgFUS thalamotomy is a cost-effective treatment for patients with TDPD, particularly over continued medical therapy. While MRgFUS remains competitive with DBS, the cost-effectiveness advantage is less substantial. These results will help inform the integration of this technology in the healthcare system.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".