ArticleScientific reports2025
Prediction of personalized antiseizure medications response based on clinical signatures in epilepsy.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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
1 citing paper in PubMed.
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
Despite the increasing number of available antiseizure medications (ASMs), optimal medical therapy is still a process of trial and error. We aimed to predict the responsiveness of various ASMs based on initial tests using artificial intelligence. The study consisted of 2586 patients fulfilling the following criteria: (1) first visit to the epileptologists from 2008 to 2017, (2) a diagnosis of epilepsy, and 3) ≥ three years of follow-up duration. The clinical characteristics, ASM history, seizure frequency, laboratory, EEG, and MRI results, were collected. Machine algorithms were utilized to predict the responsiveness of specific regimens. Valproate showed the highest area under curve (AUC), 0.636. The AUCs of levetiracetam, oxcarbazepine, and lamotrigine were 0.614, 0.633, and 0.674. The AUCs of common dual regimens were 0.543 for levetiracetam + oxcarbazepine, 0.454 for levetiracetam + valproate, and 0.583 for levetiracetam + lamotrigine. Levetiracetam + carbamazepine showed the highest AUC, 0.686. In Shapley Additive exPlanations analysis, seizure type significantly impacted prediction performance for valproate responsiveness, and onset age and disease duration for lamotrigine. The prediction performances for the response based on initial data differ according to ASMs. An enormous dataset from a multicenter would improve the prediction power of ASM responsiveness in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.