118 A Cost Effectiveness Analysis of Two Treatment Strategies for Trigeminal Neuralgia in Ontario
Bibliographic record
Abstract
INTRODUCTION: Trigeminal neuralgia is a debilitating disease affecting 4-27/100000 people annually. In Ontario, over 2000 patients will be cared for through a universal government payer model. Interventions for profound pain include medical and surgical therapies, among other options. The global expected cost of these approaches is unknown. 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 Under the Health Acct. Academic literature was used to estimate unavailable items. A cost-benefit Markov model was created for each strategy with literature-based event rates for annual cycles from year one to five, followed by a linear recurrent cycle from year six to ten. Incremental cost-effectiveness ratios (ICERs) were calculated based on incremental cost in $CAD in 2022 per patient with a pain free year. RESULTS: Base case cost per patient was $10866 at ten years in the “MVD first” group and $10710 in the “carbamazepine first” group. The break-even point is slightly longer than ten years. Surgical cost is significantly higher at year one, at $9733 versus $1903. Ten year ICER was $1014 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 was due to surgery cost, medication failure rate, and medication cost. CONCLUSIONS: Economic beneift is established to a “MVD first” strategy in the Ontario context with strict superiority beyond the ten-year horizon. If an arbitrary ICER of $50000 is used, benefit is established at four years. MVD could be considered broadly as a cost-effective and cost-saving measure in patients expected to live beyond these horizons.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".