A cost effectiveness analysis of two treatment strategies for trigeminal neuralgia in Ontario
Bibliographic record
Abstract
Background: Trigeminal neuralgia (TN) is a debilitating disease with an annual incidence of approximately 4-27/100,000. In Ontario, over 2000 patients receive interventions for profound pain, including medical and surgical therapies. The global expected cost of these approaches is unknown. This study aims to analyze the cost-effectiveness of one surgical therapy, microvascular decompression (MVD), compared with the best medical therapy (carbamazepine) as first-line therapy. Methods: Costs were gathered from the Canadian Institute for Health Information, Ontario Drug Benefit Formulary, and Ontario Ministry of Health Schedule of Benefits for Physician Services. Academic literature was used to estimate unavailable items. A cost-benefit Markov model was created for each strategy with literature-based rates for annual cycles from years 1 to 5, followed by a linear recurrent cycle from years 6 to 10. Incremental cost-effectiveness ratios (ICERs) were calculated based on the incremental cost in 2022 Canadian Dollars (CAD) per pain-free year. Results: Base case cost per patient was $10,866 at 10 years in the "MVD first" group and $10,710 in the "carbamazepine first" group. Ten-year ICER was $1,104 for "MVD first," with strict superiority beyond this time point. One-way deterministic sensitivity analysis for multiple factors suggested the highest cost variability and ICER variability were due to surgery cost, medication failure rate, and medication cost. Conclusion: Economic benefit is established for a "MVD first" strategy in the Ontario context with strict superiority beyond the 10-year horizon. If a cost-effectiveness threshold of $50,000 per pain-controlled year is used, the benefit is established at 4 years.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".