An Evidence-based Guideline for Pediatric Prehospital Seizure Management Using GRADE Methodology
Bibliographic record
Abstract
OBJECTIVE: The objective of this guideline is to recommend evidence-based practices for timely prehospital pediatric seizure cessation while avoiding respiratory depression and seizure recurrence. METHODS: A multidisciplinary panel was chosen based on expertise in pediatric emergency medicine, prehospital medicine, and/or evidence-based guideline development. The panel followed the National Prehospital EBG Model using the GRADE methodology to formulate questions, retrieve evidence, appraise the evidence, and formulate recommendations. The panel members initially searched the literature in 2009 and updated their searches in 2012. The panel finalized a draft of a patient care algorithm in 2012 that was presented to stakeholder organizations to gather feedback for necessary revisions. RESULTS: Five strong and ten weak recommendations emerged from the process; all but one was supported by low or very low quality evidence. The panel sought to ensure that the recommendations promoted timely seizure cessation while avoiding respiratory depression and seizure recurrence. The panel recommended that all patients in an active seizure have capillary blood glucose checked and be treated with intravenous (IV) dextrose or intramuscular (IM) glucagon if <60 mg/dL (3 mmol/L). The panel also recommended that non-IV routes (buccal, IM, or intranasal) of benzodiazepines (0.2 mg/kg) be used as first-line therapy for status epilepticus, rather than the rectal route. CONCLUSIONS: Using GRADE methodology, we have developed a pediatric seizure guideline that emphasizes the role of capillary blood glucometry and the use of buccal, IM, or intranasal benzodiazepines over IV or rectal routes. Future research is needed to compare the effectiveness and safety of these medication routes.
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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 | 0.036 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".