Improving migraine headache management in emergency departments: The time has come
Bibliographic record
Abstract
Migraine is an exceedingly common chronic condition in both adult and paediatric populations. Many patients with migraine headache manage symptoms and exacerbations with simple medical management, rest and without the need to access additional health services. At times when interventions at home fail, patients with moderate–severe migraines present to the Emergency Department (ED) for care. For example, 7% of Americans with migraine headaches report using the ED or Urgent Care Center for treatment of severe headaches within the previous 12 months (1). Moreover, in the USA evidence suggests that headaches account for approximately 2.2% of annual ED visits (2). Patients who present to the ED with migraine exacerbations can be treated with a myriad of agents and it is not surprising that substantial practice variation among and within EDs has been documented (3–5). For example, previous research shows that 20 disparate parental agents have been used to treat acute migraine in US EDs in the past (2). In part, this may be due to the fact that the
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.031 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".