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Record W4416415753 · doi:10.3389/fcogn.2025.1692578

Cognitive rehabilitation among long COVID patients using vibratory and auditory treatment (VAT) is linked to BDNF

2025· article· en· W4416415753 on OpenAlexaff
Abdullah Mosabbir, Jed A. Meltzer, Arkady Uryash, Erika L. Beroncal, Ana C. Andreazza, Lee Bartel

Bibliographic record

VenueFrontiers in Cognition · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsCentre for Addiction and Mental HealthBaycrest HospitalUniversity of Toronto
FundersLiverpool John Moores UniversityUniversity College London
KeywordsCognitionCoronavirus disease 2019 (COVID-19)RehabilitationBrain-derived neurotrophic factorSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Neurotrophic factors

Abstract

fetched live from OpenAlex

Cognitive dysfunction occurs in around 40% of long COVID (LC) patients, and in many cases appears second only to fatigue in prevalence. Vibratory and auditory treatment (VAT) within the gamma range has demonstrated improvements in symptoms associated with cognition and fatigue. In this open-label pilot study, we tested the effects of VAT on measures of cognition and fatigue in LC. Twenty patients were randomly divided into a treatment and a control group. Symptoms were monitored remotely through mobile apps and in-person visits before and after the treatment period. The treatment group received a device generating 40 Hz of VAT to take home and use every day from Monday to Friday for 4 weeks (i.e., 20 sessions over 28 days), whereas the control group did not use any device but followed the same data collection procedures. This study found that after 4 weeks of VAT, participants with LC exhibited increased performance in selective attention and response inhibition, an increased amount of circulating brain-derived neurotrophic factor (BDNF), and a reduced resting heart rate. We propose that VAT may be a useful rehabilitative tool for LC as well as other targeted populations that seek improvements in cognition or general health but are compromised immunologically or physically.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.300
Teacher spread0.290 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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