P.006 Barriers and risk factors for emergency room visits vs smartphone app use for migraine in Canada and the United States
Bibliographic record
Abstract
Background: Migraine affects more than 1 billion people, with attacks triggered by a variety of factors. Knowledge of environmental triggers for migraine attacks is limited, and has mostly been studied via emergency room (ER) visits. There are significant barriers and delays for attending ER for migraine treatment, which create challenges for estimating causal links to environmental exposures. We assessed whether smartphone app records may have fewer barriers and reduced lags. Methods: American and Canadian participants completed an online survey about their migraine attacks, smartphone app use, and ER visits. Results: Among 308 participants, barriers to visiting ER were similar in both countries, except for financial concerns in the US. About half of participants who attended ER also recorded the attack in a diary or app. Whereas migraine patients often present to ER 7+ days after onset, records in a smartphone app dataset were created within 2 days of onset. Conclusions: Although not all severe migraine attacks are recorded by smartphone users, smartphone app records may have fewer barriers to creation and shorter time lags compared to ER visit records, making them a rich source of data for research on transient neurologic health outcomes and environmental exposures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".