Canadian Headache Society systematic review and recommendations on the treatment of migraine pain in emergency settings
Bibliographic record
Abstract
BACKGROUND: There is a considerable amount of practice variation in managing migraines in emergency settings, and evidence-based therapies are often not used first line. METHODS: A peer-reviewed search of databases (MEDLINE, Embase, CENTRAL) was carried out to identify randomized and quasi-randomized controlled trials of interventions for acute pain relief in adults presenting with migraine to emergency settings. Where possible, data were pooled into meta-analyses. RESULTS: Two independent reviewers screened 831 titles and abstracts for eligibility. Three independent reviewers subsequently evaluated 120 full text articles for inclusion, of which 44 were included. Individual studies were then assigned a US Preventive Services Task Force quality rating. The GRADE scheme was used to assign a level of evidence and recommendation strength for each intervention. INTERPRETATION: We strongly recommend the use of prochlorperazine based on a high level of evidence, lysine acetylsalicylic acid, metoclopramide and sumatriptan, based on a moderate level of evidence, and ketorolac, based on a low level of evidence. We weakly recommend the use of chlorpromazine based on a moderate level of evidence, and ergotamine, dihydroergotamine, lidocaine intranasal and meperidine, based on a low level of evidence. We found evidence to recommend strongly against the use of dexamethasone, based on a moderate level of evidence, and granisetron, haloperidol and trimethobenzamide based on a low level of evidence. Based on moderate-quality evidence, we recommend weakly against the use of acetaminophen and magnesium sulfate. Based on low-quality evidence, we recommend weakly against the use of diclofenac, droperidol, lidocaine intravenous, lysine clonixinate, morphine, propofol, sodium valproate and tramadol.
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 | 0.052 | 0.179 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".