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Record W7065397104

Effect of High-Frequency Repetitive Transcranial Magnetic Stimulation on Patients with Post-Stroke Comorbid Cognitive Impairment and Depression

2024· article· en· W7065397104 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDepression (economics)Transcranial magnetic stimulationRating scaleCognitive impairmentCognitionRehabilitationCognitive trainingPost-stroke depressionElectroconvulsive therapy
DOInot available

Abstract

fetched live from OpenAlex

ObjectiveTo observe the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) on patients with post-stroke comorbid cognitive impairment and depression (PSCCID).MethodsA total of 30 patients with PSCCID were treated in the department of rehabilitation medicine of Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital from January 2018 to December 2020 were randomly divided into control group and observation group, with 15 cases in each group. The control group received routine medication treatment and routine rehabilitation, including exercise therapy (40 minutes a time, once a day, five days a week), occupational therapy (30 minutes a time, once a day, five days a week), and cognitive training (30 minutes a time, once a day, five days a week), for four weeks. The observation group received high-frequency rTMS treatment (stimulation of the left dorsolateral prefrontal cortex, 10 Hz, 100% resting motion threshold, 20 minutes a time, once a day, five days a week) for four weeks, in addition to the treatment received by the control group. Before and after treatment, the Montreal cognitive assessment (MoCA) and the mini-mental state examination (MMSE) scores were used to assess cognitive function. The 17-item Hamilton depression rating scale (HAMD-17) was used to assess depression. The 3.0T magnetic resonance imaging system was used to scan the patient's brain, and voxel-based morphological analysis was used to analyze the changes of gray matter density in local brain areas.ResultsCompared with those before treatment, the MMSE and MoCA scores were higher and the HAMD-17 score was lower in both groups after treatment, and the differences were statistically significant (<italic>P</italic>&lt;0.05). Compared with the control group, the MMSE and MoCA scores were higher in the observation group after treatment, and the HAMD-17 score were lower, and the differences were statistically significant (<italic>P</italic>&lt;0.05). Compared with the control group, gray matter density in the left head and face regions of precentral gyrus, and the left caudal area of middle temporal gyrus of the observation group were higher, while gray matter density in the left medioventral occipital cortex and right middle frontal gyrus were lower, and the difference was statistically significant (<italic>P</italic>&lt;0.05).ConclusionHigh-frequency rTMS can improve cognitive function and depression of patients with PSCCID, and the mechanism may be related to the increase of the gray matter density in local brain regions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.417
Teacher spread0.382 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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