Diagnosis and Management of Adult Status Epilepticus in Resource-Limited Settings
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Status epilepticus (SE) is the leading cause of death in patients with epilepsy, and it affects people in low/middle-income countries (LMICs) at a much higher rate. There is likely a significant gap between the recommended diagnosis and treatment of SE and current practices in resource-limited settings. We conducted a systematic literature review to determine how convulsive and nonconvulsive SE in adults is diagnosed and managed in LMICs. METHODS: All relevant articles from Embase, Medline, PubMed, and the Virtual Health Library Regional Portal databases, published before September 16, 2024, were included. Studies needed to take place in LMICs and include treatment and outcomes of patients with SE. This review followed the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. The risk of bias was assessed using the Risk of Bias in Randomized Trials and Risk of Bias in Non-randomized Studies of Interventions tools. RESULTS: Our review included 23 studies from 3 continents including 1,526 patients, with most of the studies conducted in Asia. There is a lack of literature from Africa and surrounding the topic of nonconvulsive SE. The commonest etiology of SE was an acute symptomatic cause (21%-88%), with encephalitis predominating overall. Diagnostic and management practices varied greatly, dictated by local availability of drugs and expertise, rather than guidelines. First-line benzodiazepines were routinely underdosed while older and cheaper second-line antiseizure medications, such as valproic acid, phenytoin, and phenobarbital, were more frequently administered. In addition, there was a general lack of access to continuous EEG monitoring, with only 5 studies from tertiary-level centers in Asia reporting its usage. Mortality outcomes of up to 42.6% are higher in comparison with high-income countries. DISCUSSION: The heterogeneity in management practices of SE in LMICs highlights the lack of consistent treatment, with very few studies from Africa and Latin America available in the literature. This contributed to the limitations of this review, with only a small region of countries (mostly from Asia) represented and retrospective review of clinical records predominantly used. The nonuniformity of diagnostic and management practices in SE has highlighted the need for clinically appropriate guidelines in LMICs.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".