Bibliographic record
Abstract
Although an objective of migraine therapy is to eliminate visits to the emergency department (ED), many patients still visit this unit to seek relief from an acute attack. These patients will encounter considerable variation in treatment1 since several treatments have Level 1 evidence for efficacy, and Grade A recommendations for clinical use. These include dopamine agonists (metoclopramide, prochlorperazine, and chlorpromazine), dihydroergotamine, subcutaneous sumatriptan, and ketorolac.2 Published guidelines based on randomized trials recommend non-narcotic medications as first-line agents to treat severe migraine but the use of opioids to treat migraine in the ED is common.3 In a recent study the majority of ED patients (59.6%) received narcotics as a first-line agent.3 A survey of three nonaffiliated US EDs found that 38% of patients with migraine received meperidine initially.1 Steroids have been used to treat difficult migraine attacks since as early as 1967.4 In a small nonrandomized study in 1986, Gallagher reported a benefit for dexamethasone 8 mg IV as adjuvant therapy for migraine in the ED.5 Several double-blind randomized controlled trials have …
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".