Evidence map›Paper›PMID 37736066›Full record

ArticleNeurology. Clinical practice2023

The Yield of Ambulatory Video-EEG: Predictors of Successful Event Capture.

Caralynn Li, Ushtar Amin, Angelica Rivera-Cruz, Alfred T Frontera, Selim R Benbadis

Erratum issuedAbstract read
In one paragraph

Article in Neurology. Clinical practice, 2023. 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Erratum: Missing Full Disclosures.Neurology. Clinical practice · 2025
    Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Caralynn LiDepartment of Neurology, University of South Florida Morsani College of Medicine.ORCID https://orcid.org/0000-0002-4799-6111
Ushtar AminDepartment of Neurology, University of South Florida Morsani College of Medicine.ORCID https://orcid.org/0000-0002-8155-3752
Angelica Rivera-CruzDepartment of Neurology, University of South Florida Morsani College of Medicine.ORCID https://orcid.org/0009-0007-4688-0364
Alfred T FronteraDepartment of Neurology, University of South Florida Morsani College of Medicine.ORCID https://orcid.org/0000-0001-5196-6559
Selim R BenbadisDepartment of Neurology, University of South Florida Morsani College of Medicine.ORCID https://orcid.org/0000-0003-0214-5492

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: The purpose of this study was to assess the likelihood of capturing a patient's typical event in question on ambulatory video-EEG monitoring (AVEM) based on certain baseline patient or event characteristics. Methods: We retrospectively reviewed 300 studies that resulted between June 2021 and August 2022 ordered by adult epileptologists. Patients were included in event analysis if the study was ordered for the purpose of capturing an event (and excluded for all other purposes). Results: A total of 149 studies were included in event analysis. Sixty-eight patients (46%) had their typical events captured on AVEM. Diagnosis was an epileptic seizure in 17 patients (25%), psychogenic nonepileptic seizure in 7 (10%), and other nonepileptic events in 44 (65%). Regarding event frequency, for patients who on average had daily events, 84% had events captured, which corresponds to a significantly increased odds ratio (OR 17.90, 95% CI 7.55-42.44, Discussion: Certain baseline characteristics can increase or decrease the pretest probability of capturing a typical event on AVEM, particularly the frequency of events and number of ASMs. This can be useful information for clinicians before ordering a study so that resources can be properly allocated.

Identifiers

PMID37736066
PMCPMC10511269

What OpenQuestion holds

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Registered trials

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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.