ReviewJournal of central nervous system disease2023
The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?
Review in Journal of central nervous system disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Barriers and enablers to antiseizure medication adherence in children with epilepsy: a systematic review using the theoretical domains framework (TDF).BMJ paediatrics open · 2026Pooled it
- Anti-seizure Effects and Mechanisms of Berberine: A Systematic Review.Current pharmaceutical biotechnology · 2024Pooled it
- Efficacy and Safety of Adjunctive Cenobamate in Chinese Participants with Focal Seizure.Advances in therapy · 2026Trial
- Antiepileptic and antiepileptogenic effects ofIBRO neuroscience reports · 2026Article
- Is Ultra-Long-Term Subscalp EEG Ready to Go "Solo"? Transforming the Landscape of Antiseizure Medication Management.Epilepsy currents · 2026Article
- Long-Term Follow-Up of Levetiracetam Monotherapy Versus Add-On Therapy in Pediatric Epilepsy.Children (Basel, Switzerland) · 2026Article
- Observational
- The potential of laminar functional MRI in refining the understanding of epilepsy in humans.Brain : a journal of neurology · 2025Review
- A research roadmap for SCN8A-related disorders: addressing knowledge gaps and aligning research priorities across stakeholders.Orphanet journal of rare diseases · 2025Article
- Trends in prescription of new antiseizure medications in a single center in Latin America: evidence of clinical practice.Frontiers in neurology · 2025Article
- Anti-seizure Medication Induced Cognitive Impairment in Children with Epilepsy: A Narrative Review.Iranian journal of child neurology · 2025Review
- Adverse effects of antiseizure medications: a review of the impact of pharmacogenetics and drugs interactions in clinical practice.Frontiers in pharmacology · 2025Review
- Cenobamate for Difficult-to-Treat Epilepsy - Selected Case Vignettes.Neuropsychiatric disease and treatment · 2025Article
- Relative Bioavailability Study of Midazolam Intramuscularly Administered with the Needle-Free Auto-Injector ZENEONeurology and therapy · 2024Article
- Narrative Review of Brivaracetam: Preclinical Profile and Clinical Benefits in the Treatment of Patients with Epilepsy.Advances in therapy · 2024Review
- Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population.Clinical and translational science · 2024Review
- Therapeutic approaches targeting seizure networks.Frontiers in network physiology · 2024Review
- A prospective, observational, multicentre study to evaluate the efficacy of brivaracetam as adjuvant therapy for epilepsy: The Bravo study.Drugs in context · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient's related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.