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Record W4417274491 · doi:10.1212/wnl.0000000000214466

Management of Functional Seizures Practice Guideline Executive Summary

2025· article· en· W4417274491 on OpenAlexaff
Benjamin Tolchin, Laura H. Goldstein, Markus Reuber, Jon Stone, David L. Perez, W. Curt LaFrance, Aaron D. Fobian, James Dorman, Le H. Hua, Z. Paige L'Erario, Sara J. Swanson, Peter Gilli, Bridget Mildon, Courtney Takahashi, Kylie Botchway-Doe, Katherine Hamel, Heather M. Silsbee, Maryam Oskoui

Bibliographic record

VenueNeurology · 2025
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsMcGill University
FundersNational Institute of Mental HealthUniversity of OxfordMotor Neurone Disease AssociationU.S. Department of Veterans AffairsNational Institute for Health and Care ResearchNational Institutes of HealthSwebilius FoundationUCB PharmaSidney R. Baer, Jr. FoundationNational Institute of Diabetes and Digestive and Kidney DiseasesU.S. Department of Defense
KeywordsPsychological interventionPsychosocialGuidelineMultidisciplinary approachFunctional impairmentMEDLINEQuality of life (healthcare)

Abstract

fetched live from OpenAlex

This guideline provides evidence-based recommendations for clinicians, patients, and other stakeholders on the management of functional seizures. Following a National Academy of Medicine-compliant process, a multidisciplinary panel conducted a systematic review and integrated the findings with the authors' clinical experience to develop recommendations. A systematic review of the available evidence from first published articles to February 25, 2025, identified 12 Class II-III studies. The review found that psychological interventions are possibly effective in increasing the likelihood of achieving freedom from functional seizures, decreasing the frequency of functional seizures, decreasing anxiety, and improving health-related quality of life and psychosocial functioning for individuals with functional seizures. Key recommendations state that, when evaluating patients with seizure-like episodes, clinicians should seek historical and semiological information (including smartphone videos) from both patients and witnesses and may obtain video-EEG of all typical seizure-like episodes where feasible. Clinicians should evaluate patients diagnosed with functional seizures for co-occurring psychiatric disorders and co-occurring epilepsy. Clinicians should adhere to universal standards of care for patients, including speaking respectfully, refraining from unnecessary harm, and avoiding stigmatizing behavior. Clinicians should provide a specific diagnostic label and rationale for the diagnosis, should engage in shared decision making regarding the treatment plan, and should provide continuity of care to individuals diagnosed with functional seizures. When psychological interventions for functional seizures are indicated, clinicians should counsel patients regarding the potential benefits and risks of these interventions and should refer interested and appropriate patients to these interventions for the treatment of functional seizures. Clinicians should involve family, caregivers, or others in the social support network in the psychological treatment of individuals with functional seizures. Clinicians should not prescribe benzodiazepines or antiseizure medications for patients with functional seizures without co-occurring epilepsy or another indication for these medications and should counsel patients regarding the potential risks and lack of evidence of benefit for using these medications for functional seizures. Clinicians should taper off antiseizure medications for patients with functional seizures and without another indication for these medications. The guideline also identifies gaps in the available evidence and outlines potentially clinically impactful avenues for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0060.006
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0400.024

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.307
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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