Lidocaine for Status Epilepticus in Pediatrics
Bibliographic record
Abstract
BACKGROUND: Our goal was to perform a systematic review of the literature on the use of intravenous lidocaine in pediatrics for status epilepticus (SE) and refractory status epilepticus (RSE) to determine its impact on seizure control. METHODS: All articles from MEDLINE, BIOSIS, EMBASE, Global Health, HealthStar, Scopus, Cochrane Library, the International Clinical Trials Registry Platform (inception to November 2014), and gray literature were searched. The strength of evidence was adjudicated using both the Oxford and Grading of Recommendations Assessment, Development, and Evaluation methodologies by two independent reviewers. RESULTS: Overall, 20 original studies were identified, with 19 manuscripts and one meeting abstract. Two hundred and thirty-five pediatric patients were treated for 252 episodes of SE/RSE. Patients had varying numbers of antiepileptic drugs (two to eight) on board before lidocaine therapy. During 20 of the 252 (7.9%) episodes of SE/RSE, phenytoin was on board. The dose regimen of lidocaine varied, with some using bolus dosing alone; others used a combination of bolus and infusion therapy. Overall, 60.0% of seizures responded to lidocaine, with complete cessation and greater than 50% reduction seen in 57.6% and 12.3%, respectively. Patient outcomes were sparingly reported. CONCLUSIONS: There currently exists Oxford level 2b, Grading of Recommendations Assessment Development, and Evaluation C evidence to support the consideration of lidocaine for SE and RSE in the pediatric population. Further prospective studies of lidocaine administration in this setting are warranted.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".