Public Drug Coverage and Its Impact on Triptan Use Across <scp>C</scp> anada: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: Public drug coverage for triptan medications varies across jurisdictions in Canada, which may lead to differences in usage patterns and patient risk for medication overuse headache. METHODS: We conducted a population-based, cross-sectional analysis of publicly funded triptan use in seven provinces across Canada from January 1, 2012 to December 31, 2012. All patients who had filled at least one prescription for a triptan during the study period were included. We defined quantity limits of 6, 12, and 18 triptan units per month to assess the prevalence of high volumes of triptan use, which may place patients at risk for medication overuse headaches, and compared this prevalence between provinces with different funding restrictions. RESULTS: We identified 14,085 publicly funded users of triptans in 2012 in the seven provinces studied, 82.5% of whom were aged less than 65 years (N = 11,631). The prevalence of triptan use ranged substantially by province, from 0.04% in Ontario to a maximum of 1.0% in Manitoba (P < .001). Furthermore, the percentage of patients in each province using more than 6, 12, or 18 units per month differed significantly between provinces (P < .001). In particular, the percentage of patients treated with more than 6 units per month ranged from as low as 2.1% in Saskatchewan to 43.8% in Ontario. CONCLUSIONS: Differing public drug reimbursement criteria for triptans may be one contributing factor that has led to our observation of considerable variation in both prevalence of triptan prescribing and potential overuse of these medications. We offer that monthly quantity limits may be considered as a tool to decrease risks for medication overuse headache.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".