Evaluating Levetiracetam Weight-Based Dosing in Benzodiazepine-Refractory Status Epilepticus
Bibliographic record
Abstract
BACKGROUND: Levetiracetam (LEV) is an antiseizure medication (ASM) used as a second line after benzodiazepines for status epilepticus treatment. Current literature lacks direct head-to-head comparisons between different LEV loading dose strategies, leading to uncertainty about superior dosing methods and thus clinical practice variations. METHODS: A retrospective cohort study was designed to compare efficacy and safety of low (<30 mg/kg) versus high (≥30 mg/kg) weight-based LEV loading doses in adults with benzodiazepine-refractory status epilepticus (BRSE). The primary outcome of this study was termination of BRSE. No requirement for additional ASM after LEV was a surrogate for BRSE termination. Secondary endpoints included endotracheal intubation, intensive care unit (ICU) admission, 30-day all-cause mortality and adverse drug reactions. Statistical analysis included discrete and inferential statistics, including logistic regression and win-ratio analysis, to control for potential confounding variables. RESULTS: Of the 106 patients included in this study, 54 (51%) did not require additional ASM after LEV, thereby achieving seizure termination. There was a higher proportion of patients with seizure termination in the higher weight-based dosing group as compared to the lower weight-based group (66% vs 40%, respectively; aOR 3.07; 95% CI: 1.36-7.21). There were lower rates for endotracheal intubation, ICU admission and all-cause mortality in the higher dosing group. Adverse events were comparable between the both groups. CONCLUSION: LEV's high weight-based loading dose strategy (≥30 mg/kg) is more effective in the termination of BRSE as compared to the lower weight-based loading dose strategy (<30 mg/kg).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".