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Record W4407935001 · doi:10.1016/j.brs.2024.12.523

Enhancing Antiepileptic Drug Efficacy through Focused Ultrasound in an Epilepsy Model

2025· article· en· W4407935001 on OpenAlexaboutno aff
Jiwon Baek, Hyeon-Ju Lee, S. H. Lim, Chanho Kong, Bong Soo Kim, Won Seok Chang

Bibliographic record

VenueBrain stimulation · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAntiepileptic drugEpilepsyMedicineDrugPharmacologyPsychiatry

Abstract

fetched live from OpenAlex

Montreal Cognitive Assessment.Secondary measures included response rate of Montreal Cognitive Assessment and changes in the Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Mini-Mental State Examination at the end of treatment.Results: Of forty-five randomized participants, forty-one were analyzed.Both the high-dose (estimated difference4.65,p<0.0001, 95% confidence interval3.31-5.99)and low-dose (estimated difference2.15,p0.0024, 95% confidence interval0.81-3.50)treatment groups showed significant improvements in Montreal Cognitive Assessment scores.Particularly, the high-dose group demonstrated better cognitive recovery and a higher recovery rate than the low-dose and sham groups, indicating a dosedependent effect for post-stroke cognitive recovery.Moreover, dosedependent improvements were similarly observed in Wechsler Memory Scale, Wechsler Adult Intelligence Scale and Mini-Mental State Examination, suggesting a consistent dose-dependent effect on memory, intelligence, and mental state.Importantly, no serious adverse events were reported. Conclusion:This study highlights dose-dependent effects in the cognitive recovery post-stroke through intermittent theta burst stimulation targeting the individualized frontoparietal network.Importantly, high-dose intermittent theta burst stimulation was found to be safe and effective for patients with post-stroke cognitive impairments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.417
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.363
Teacher spread0.337 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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