Measuring Migraine in Canada and the USA: An Online Survey of Emergency Room and Smartphone Application Use
Bibliographic record
Abstract
BACKGROUND: Knowledge of environmental triggers for migraine attacks is limited and has mostly been acquired by studies using emergency room (ER) visits. However, it is unlikely that ER visits are a random sample of migraine events, even within strata of migraine severity. Additionally, time lags between attack onset and ER visits may vary across the population, posing challenges for assessing causal links of migraine with community-level or ecologic exposures. OBJECTIVE: Our objective was to assess the relationship between demographic and geographic measures and self-reported migraine-related ER visits. METHODS: We analyzed a targeted non-probability survey of ER use related to migraine in Canada and the USA. The 18-question online survey addressed ER use and behaviors related to recording attacks. RESULTS: The final dataset included 389 respondents (Canada = 164 [42.2%], USA = 225 [57.8%]); 51 (13.1%) were Migraine Buddy app users who shared their diaries. In both countries, participants reported similar migraine symptoms. Barriers to attending the ER included cost and wait times. There was more variability in delays between attack onset and arrival to the ER than between onset and recording in the smartphone app. Younger participants and participants living in Canada were significantly more likely to present to the ER. CONCLUSION: The sample of patients presenting to the ER for migraine may be biased toward younger patients and depend on the jurisdiction. Smartphone app records may have fewer barriers to creation and more consistent time lags compared to ER visit records.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".