Evidence map›Paper›PMID 40692168›Full record

ArticleAnnals of clinical and translational neurology2025

The Road Not Taken: Misclassifying an Anti-Seizure Medication as a Failure.

Christopher N Henry, Daniel M Goldenholz

Abstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Christopher N HenryDepartment of Neurology, Children's Hospital of Richmond at VCU Health, Virginia, United States.ORCID 0000-0003-1066-1366
Daniel M GoldenholzBeth Israel Deaconess Medical Center, Department of Neurology, United States.ORCID 0000-0002-8370-2758

Funding

Non-Invasive Seizure Forecasting System Using E-Diaries, Internal and External FactorsK23NS124656 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Daniel M Goldenholz · 2022 to 2026
$954k
NIH HHS K23NS124656NINDS NIH HHS K23 NS124656
6 · The paper itself

Abstract

objectiveTo quantify how often anti-seizure medications (ASMs) appear ineffective yet provide benefit when considering seizure frequency (SF) variability.

methodsWe used the CHOCOLATES seizure diary simulator to generate 100,000 patient seizure diaries that reflect natural SF variation in a heterogeneous population. Medication effect was modeled as a 20% average SF reduction (standard deviation 10%). We identified how many patients with an observed ≥ 25% SF increase (apparent worsening) actually had a true ≥ 10% SF reduction (vs. no medication), and how many with an observed ≥ 50% SF reduction (apparent responders) would have shown < 0% reduction if not taking the ASM. We also quantified how many individuals who had apparent worsening were actual worsening (> 0% SF increase vs. no medication).

resultsSimulations closely matched real-world ASM trials, showing a median SF reduction of 36% with ASM versus 17% with placebo; 35% of patients on ASM achieved ≥ 50% SF reduction versus 20% on placebo. Apparent worsening occurred in 12%; among these, 76% were true improvers. Of the apparent responders, 12% were true nonresponders. Only 4% of the individuals with apparent worsening truly worsened compared to no medication.

interpretationSF variability can lead to significant misclassification of ASM benefit. Many patients labeled as having "failed" an ASM trial were likely receiving meaningful benefit and may warrant reconsideration of the medication. Prospective clinical studies are needed to determine how best to account for SF variability and refine the interpretation of treatment response in epilepsy management.

Indexed as

AnticonvulsantsSeizuresAdultFemaleHumansMaleMiddle AgedTreatment FailureAnticonvulsantsanti‐seizure medicationbioinformaticsepilepsysimulationstatistics

Identifiers

PMID40692168
PMCPMC12516234

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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.