Treatment of Migraine in Canada With Naratriptan: A Cost‐Effectiveness Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of naratriptan for the treatment of migraine in Canada. BACKGROUND: The substantial disability brought on by migraine, coupled with the high prevalence of this disorder, leads to substantial costs. Naratriptan is a newly developed triptan shown to be effective in the treatment of migraine. METHODS: Monte Carlo modeling techniques were used to simulate the experience of Canadian migraineurs over the course of 1 year. Data from a multinational study comparing oral naratriptan 2.5 mg to customary therapies were used in the cost-effectiveness analysis. RESULTS: Naratriptan leads to an annual reduction in symptom duration of 225 hours compared to customary therapy not including other triptans. Reductions in lost productivity yield savings of Can $390 (1998 Canadian dollars) relative to customary therapy, which exceed the increase in drug costs resulting in overall savings of Can $109 per year. CONCLUSIONS: The use of naratriptan in the treatment of migraine is an economically attractive option, leading to savings in overall costs. Increases in drug costs seem acceptable in light of reductions in symptom duration.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".