ArticleEpilepsia2024
Prospective validation of a seizure diary forecasting falls short.
Article in Epilepsia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Article
- Deployable seizure forecasting requires clinically meaningful performance: Response to Stirling et al.Epilepsia · 2026Article
- Does Missing Medication Acutely Change Seizure Risk? A Prospective Study.Annals of neurology · 2026Article
- Article
- Early detection of antiseizure medication inefficacy using an implantable continuous EEG system and a personalized model: a case study.Epilepsy & behavior reports · 2025Article
- Rhythms in Longitudinal Thalamic Recordings are Linked to Seizure Risk.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Seizure prediction and forecasting: a scoping review.Current opinion in neurology · 2025Article
- Necessary for seizure forecasting outcome metrics: Seizure frequency and benchmark model.Epilepsy research · 2024Article
- Necessary for seizure forecasting outcome metrics: seizure frequency and benchmark model.medRxiv : the preprint server for health sciences · 2024Article
- The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.Frontiers in neurology · 2024Review
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
objectiveRecently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.
methodsWe recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI.
resultsThe AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.
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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.