Drug-induced seizures in children and adolescents presenting for emergency care: Current and emerging trends
Bibliographic record
Abstract
CONTEXT: Seizures may be the presenting manifestation of acute poisoning in children. Knowledge of the etiologic agent, or likely drug-class exposure, is crucial to minimize morbidity and optimize care. OBJECTIVES: To describe the agents most commonly responsible for pediatric drug-induced seizures, whose evaluation included a medical toxicology consultation in the United States. METHODS: Using the 37 participating sites of the Toxicology Investigators Consortium (ToxIC) Case Registry, a cross-country surveillance tool, we conducted an observational study of a prospectively collected cohort. We identified all pediatric (younger than 18 years) reports originating from an Emergency Department (ED) which included a chemical or drug-induced seizure, and required a medical toxicology consultation between April 1, 2010 and March 31, 2012. Results. We identified 142 pediatric drug-induced seizure cases (56% male), which represent nearly 5% of pediatric cases requiring bedside consultation by medical toxicologists. One-hundred and seven cases (75%) occurred in children aged 13-18 years, and 86 (61%) resulted from intentional ingestions. Antidepressants were the most commonly identified agents ingested (n = 61; 42%), of which bupropion was the leading drug (n = 30; 50% of antidepressants), followed by anticholinergics/antihistamines (n = 31; 22%). All antidepressant-induced seizures in teenagers were intentional and represented self-harm behavior. Sympathomimetic agents, including street drugs, represent the most common agents in children younger than 2 years (n = 4/19). CONCLUSION: Antidepressants, and specifically bupropion, are presently the most common medications responsible for pediatric drug-induced seizures requiring medical toxicology consultation in the United States. In teenagers presenting with new-onset seizures of unknown etiology, the possibility of deliberate self-poisoning should be explored, since most drug-induced seizures in this age group resulted from intentional ingestion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".