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Effects of auditory training on cognitive function in patients with stroke

2016· article· en· W3029327392 on OpenAlexaboutno aff
Jingjing Zhang, Chang‐Xiang Chen, Shuxing Li, Min Zhang, Na Dou

Bibliographic record

VenueZhonghua xingwei yixue yu naokexue zazhi · 2016
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionStroke (engine)RehabilitationMontreal Cognitive AssessmentAudio visualPhysical therapyAudiologyCognitive impairmentCognitive trainingMedicineIntervention (counseling)Physical medicine and rehabilitationCognitive InterventionPsychologyPsychiatryMultimedia

Abstract

fetched live from OpenAlex

Objective To explore the effect of TOMATIS audio training on the rehabilitation of patients with cognitive impairment after stroke. Methods A total of 80 patients with cognitive impairment after stroke were randomly divided into experimental group (40 cases) and control group (40 cases). The two groups received conventional rehabilitative nursing. The control group was given TOMATIS conventional music training (conventional frequency music without audio processing), while the experimental group was given the TOMATIS audio training ( music with audio processing). The patients were evaluated with Montreal cognitive assessment scale(MoCA) before and after intervention. Results After two-cycle TOMATIS audio training, the total score of MoCA(17.43±4.11) in experimental group were higher than that before intervention(13.48±3.28), and the differences were statistically significant (P<0.01), while the memory, executive function, visual skills, abstract thinking and total standard score significantly increased in control group(P<0.05). Scores of each item and total standard score of the experimental group were higher than those of the control group after the intervention(P<0.05). Conclusion TOMATIS audio training can improve cognitive function of patients with stroke. Key words: Stroke; Cognitive impairment; TOMATIS audio training

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.219
Teacher spread0.213 · 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
Published2016
Admission routes1
Has abstractyes

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