ArticleFrontiers in neurology2021
Prediction of Seizure Recurrence. A Note of Caution.
Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies.Epilepsia open · 2024Pooled it
- Evaluation of Cardiac Structural Changes Induced by Carbamazepine-Based Nanotherapeutics in an Experimental Epilepsy Model.Nanomaterials (Basel, Switzerland) · 2025Article
- Weak self-supervised learning for seizure forecasting: a feasibility study.Royal Society open science · 2022Article
- A Patient Perspective on Seizure Detection and Forecasting.Frontiers in neurology · 2022Article
- Seizure Susceptibility Prediction in Uncontrolled Epilepsy.Frontiers in neurology · 2021Article
- The Interplay Between Neuroinfections, the Immune System and Neurological Disorders: A Focus on Africa.Frontiers in immunology · 2021Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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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.