Pharmacological Management of Migraine by Primary Care Providers in Nova Scotia
Bibliographic record
Abstract
BACKGROUND: In Canada, the management of migraine is commonly carried out by primary care providers. Guidelines for the acute and preventative management of migraine in Canada are published by the Canadian Headache Society (CHS). There are currently limited data describing prescribing patterns among clinicians caring for patients with migraine in Canada. AIMS: Our aim for this exploratory study was to characterize the current pharmacological treatments prescribed for patients with migraine in Nova Scotia, Canada, seeking care through their primary care providers. METHODS: We conducted a retrospective cross-sectional analysis of deidentified electronic medical record (EMR) data collected from January 2019 to December 2023 from the Maritime Research Network for Family Practice (MaRNet-FP) to identify prescribing patterns for the acute and preventative management of migraine in Nova Scotia. RESULTS: In total, 3075 active patients who received a diagnosis of migraine were identified in the MaRNet-FP EMR database (6.53% of total patients). Migraine patients were predominantly female (81%) with an average age of 44 ± 16 years. Between 2019 and 2023, 50% of patients with a migraine diagnosis received a prescription for a medication that can be used for the acute management of migraine, most commonly, nonsteroidal anti-inflammatory drugs and triptans. Over the same period, 60.4% of patients were prescribed a medication that can be used for the prevention of migraine, the most common of which were anti-depressants and beta-blockers. CONCLUSION: Our findings demonstrate alignment with CHS guidelines but highlight potential undertreatment of migraine.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".