P.018 Treatment patterns and healthcare resource utilization for patients with migraine in Alberta
Bibliographic record
Abstract
Background: As the second leading cause of years lived with disability in the world, and the first in people under 50, migraine represents a major burden to healthcare systems. This study examined treatment patterns and healthcare resource utilization (HRU) in patients with migraine using real-world data from Alberta. Methods: This was a retrospective cohort study of patients with ≥1 ICD-9-CM/ICD-10-CA code for migraine or ≥1 prescription for a triptan from April 1st, 2012 to March 31st, 2018. Descriptive statistics were used to characterize the study outcomes. Results: The incidence of migraine exceeded 1,000 cases per 100,000 person-years over the study period. The mean age of the cohort (n=199,931) was 40.0, and 72.3% were women. Migraine-related HRU accounted for 3%-10% of all HRU across endpoints (e.g., ED visits, hospitalization, physician visits). One-third of the cohort were prescribed acute medications (non-steroidal anti-inflammatories, triptans or other (including opioids)), whereas fewer than one-fifth were prescribed at least one migraine preventive such as tricyclic anti-depressants (proportion: 15%), anti-convulsants (13%), beta-blockers (7%), or neurotoxins (4%). Conclusions: The low medication prescription rates and high HRU indicates the potential unmet need and high disability in patients with migraine. The impact of migraine treatment patterns on HRU is an avenue for future research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".