Bibliographic record
Abstract
Background: The Coronavirus (Sars-CoV-2, COVID-19) has been evolving its viral strains, prevalence, symptomatology, and sequelae of disease for the past three years. Although the most recognized COVID-19 symptoms involve the respiratory tract; neurological symptoms have been documented. Specifically, seizures have specifically been discussed in the literature but remain both under-recognized and under-reported in clinical practice. Aim: To review of the literature of adult patients with COVID-19 and seizures and integrate into clinical practice in the acute care environment; from presentation to the emergency Department to discharge. Methods: A narrative literature review was conducted to identify all reported clinical studies involving adult patients with COVID-19 and de novo seizures from MEDLINE, yielding 108 relevant publication titles and abstracts. Additional three relevant studies were discovered through manual search of reference lists of included studies. After excluding non-related publications, 58 publications underwent full-text review. Reporting of results was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Results: Data was organized into the following themes in the literature: prevalence of seizure occurrence in patients with COVID-19; pathophysiology discussing possible cause; CSF and EEG findings in these patients, and outcomes both in and out of hospital. Conclusion: Seizures were reported as both the presenting symptom of COVID-19 infection and a sequelae of the disease. Heterogeneity identified in both severity and pathogenesis of disease may partly account for the variability in reporting. Seizures may occur as single incidences, with no further implication to the patient or they may occur in the context of New Onset Refractory Status Epilepticus. Patients may require critical care for management of Status Epilepticus or encephalopathy with accompanying seizures. Clinician vigilance is essential in identifying COVID-19 infection in patients presenting to Emergency Services with seizures. Early recognition impacts patient care both in-hospital and at post-discharge follow-up.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".