Relative Effectiveness of Dopamine Antagonists for Pediatric Migraine in the Emergency Department
Bibliographic record
Abstract
OBJECTIVE: Migraine headaches are common in the pediatric emergency department. The mainstay of abortive treatment consists of nonsteroidal anti-inflammatories and dopamine antagonists. The objective of this study was to compare the effectiveness of 3 commonly used dopamine antagonists to abort pediatric migraine. METHODS: This was a retrospective cohort study of all patients who presented to the pediatric emergency department at a tertiary care pediatric hospital between January 2010 and December 2013. Patients were treated for a migraine headache with a combination of ketorolac and one of the following dopamine antagonists: prochlorperazine, metoclopramide, or promethazine. The primary outcome was treatment failure and receiving non-evidence-based treatment defined by the need for opioids. Secondary outcomes included pain score reduction and return visit within 48 hours. RESULTS: There were 57 patients during this period with 67 visits that met inclusion criteria: 27 (40.3%) visits in which patients were treated with prochlorperazine, 23 (34.3%) visits in which patients were treated with metoclopramide, and 17 (25.4%) visits in which patients were treated with promethazine. Across visits, the mean age was 14.5 years, and 63% were women. Opioids were given for treatment failure in 8.7% of visits in which patients received prochlorperazine, 25% in which patients received metoclopramide, and 42.8% in which patients received promethazine. Patients treated with promethazine had significantly higher odds of needing opioids and experiencing less than 50% reduction in pain score compared with prochlorperazine after adjusting for patient characteristics. CONCLUSIONS: This study suggests variable efficacy among 3 commonly used dopamine antagonists for pediatric migraine headache. Promethazine seems least effective and results in higher use of opioids compared with other available dopamine antagonists.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".