Efficacy of Dexamathasone on Cerebral Swelling and Seizures during Subdural Grid EEG Recording in Children
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of steroid treatment on cerebral swelling and seizures during subdural grid EEG (SGEEG) monitoring. METHODS: We reviewed data from 37 pediatric patients with intractable epilepsy who underwent SGEEG monitoring and divided the patients into those who received dexamethasone and those who did not. We then correlated administration of steroids to incidence of cerebral swelling on computed tomography (CT) scans and to frequency of seizures during SGEEG. RESULTS: Twenty-three patients received dexamethasone prophylactically every 6 hours (dosage range, 1-7.5 mg; mean, 3.2 mg) from the first day of SGEEG placement (group A); 14 patients received no dexamethasone (group B). Eight (21.6%) of 37 patients experienced cerebral swelling on CT: two (9%) were in group A, and six (42.9%) were in group B (p < 0.05). SGEEG monitoring time for recording habitual seizures that localized cortical areas for surgical excision was longer in group A (1-6 days; mean, 3.0) than it was in group B (1-3 days; mean, 2.2), (p < 0.05). Habitual seizures were recorded in 36 patients. One group A patient experienced obtundation due to cerebral swelling, and monitoring in this patient was discontinued. CONCLUSIONS: The prophylactic administration of steroids to pediatric patients during SGEEG monitoring is efficacious for reducing cerebral swelling. Although it decreases the frequency of habitual seizures and increases seizure-monitoring time, dexamethasone reduces the risk of complications from cerebral swelling during the SGEEG procedure.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".