Management of Pediatric Post-Concussion Headaches: National Survey of Abortive Therapies Used in the Emergency Department
Bibliographic record
Abstract
Children frequently present to an Emergency Department (ED) after concussion, and headache is the most commonly associated symptom. Recent guidelines emphasize the importance of analgesia for post-concussion headache (PCH), yet evidence to inform treatment is lacking. We sought to characterize abortive therapies used to manage refractory PCH in the pediatric ED and factors associated with treatment. A scenario-based survey was distributed to ED physicians at all 15 Canadian tertiary pediatric centers. Participants were asked questions regarding ED treatment of acute (48 h) and persistent (1 month) PCH refractory to appropriate doses of acetaminophen/ibuprofen. Logistic regression was used to assess factors associated with treatment. Response rate was 63% (137/219). Nearly all physicians (128/137, 93%) endorsed treatment in the ED for acute PCH of severe intensity, with most selecting intravenous treatments (116/137, 84.7%). Treatments were similar for acute and persistent PCH. The most common treatments were metoclopramide (72%), physiologic saline (47%), and nonsteroidal anti-inflammatory agents (NSAIDS; 35%). Second-line ED treatments were more variable. For acute PCH of moderate intensity, overall treatment was lower (102/137, 74%; p < 0.0001), and NSAIDS (48%) were most frequently selected. In multi-variable regression analyses, no physician- or ED-level factor was associated with receiving treatment, or treatment using metoclopramide specifically. Treatment for refractory PCH in the pediatric ED is highly variable. Importantly, patients with severe PCH are most likely to receive intravenous therapies, often with metoclopramide, despite a paucity of evidence supporting these choices. Further research is urgently needed to establish the comparative effectiveness of pharmacotherapeutic treatments for children with refractory PCH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".