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Record W4362561067 · doi:10.3389/fnins.2023.1137734

Analysis of cognitive function and its related factors after treatment in Meniere’s disease

2023· article· en· W4362561067 on OpenAlexaboutno aff
Jiake Zhong, Xin Li, Jia Xu, Wenjing Chen, Juanjuan Gao, Xingxing Lu, Sichao Liang, Zhenping Guo, Manlin Lu, Yunshuo Li, Haijin Yi

Bibliographic record

VenueFrontiers in Neuroscience · 2023
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionMeniere's diseaseVertigoMedicineAudiologyDiseaseCognitive declineVestibular systemPsychologyPhysical medicine and rehabilitationPsychiatryInternal medicineDementiaSurgery

Abstract

fetched live from OpenAlex

A growing body of research recently suggested the association between vestibular dysfunction and cognitive impairment. Meniere's disease (MD), a common clinical vestibular disorder, is usually accompanied by hearing loss and emotional stress, both of which may mediate the relationship between vestibule dysfunction and cognition. It is currently unknown whether the cognitive decline in MD patients could improve through treatment and how it relates to multiple clinical characteristics, particularly the severity of vertigo. Therefore, in the present study, the MD patients were followed up for 3, 6, and 12 months after treatment, and the cognitive functions, vertigo symptoms, and related physical, functional, and emotional effects of the patients were assessed using the Montreal Cognitive Assessment (MoCA) and Dizziness Handicap Inventory (DHI), aiming to explore the change in cognition before and after therapy and the correlation with various clinical features. It was found that cognitive decline in MD patients compared to healthy controls before therapy. Importantly, this cognitive impairment could improve after effective therapy, which was related to the severity of vertigo, especially in functional and physical impacts. Our results support the view that vestibular dysfunction is a potentially modifiable risk factor for cognitive decline.

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)0.001
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 score0.061
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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