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Record W4307264896 · doi:10.3389/fneur.2022.1027104

Effects of dual-task training in patients with post-stroke cognitive impairment: A randomized controlled trial

2022· article· en· W4307264896 on OpenAlexaboutno aff
Ruifeng Sun, Xiaoling Li, Ziman Zhu, Tiancong Li, Zhao Man, Linhong Mo, Wenshan Li, Xiaoshuang Xi, Peiling Huang, Weijun Gong

Bibliographic record

VenueFrontiers in Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMontreal Cognitive AssessmentCognitive trainingCognitionPhysical medicine and rehabilitationRandomized controlled trialStroke (engine)MedicineCognitive rehabilitation therapyEffects of sleep deprivation on cognitive performancePhysical therapyPsychologyCognitive impairmentInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Background Evidence for the efficacy of cognitive-motor dual-task (CMDT) training in patients with post-stroke cognitive impairment (PSCI) and no dementia is still lacking. More importantly, although some studies on the cognitive effect of CMDT training show an improvement in cognitive performance, the results are still controversial, and the intervention mechanism of CMDT training on cognitive function improvement is not clear. The main purpose of this study was to analyze the effects of CMDT training on cognitive function, neuron electrophysiology, and frontal lobe hemodynamics in patients with PSCI. Methods Here we tested the effects of CMDT training on cognitive function in PSCI patients. Forty subjects who met the criteria of PSCI were randomly assigned to control and experimental groups. CMDT training or cognitive task (CT) training was administered to each patient in the experimental and control groups, respectively. All subjects performed Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scale before and after the intervention, and the event-related potentials (ERP) and functional near-infrared spectroscopy (fNIRS) were used to evaluate the changes in neuron electrophysiology and hemodynamics. Results Forty patients were randomized across Beijing Rehabilitation Hospital Capital Medical University in Beijing. At the end of the intervention, 33 subjects completed the experimental process. The CMDT group showed significant improvement in the MMSE (P = 0.01) and MoCA (P = 0.024) relative to the CT group. The results of ERP and fNIRS showed that CMDT training could shorten the latency of P300 (P = 0.001) and the peak time of oxygenated hemoglobin (P = 0.004). The results showed that CMDT training shortened the response time of central neurons and significantly increased the rate of oxygen supply to the frontal lobe. Conclusion CMDT training in patients with PSCI improved global cognitive function, which was supported by the improved neural efficiency of associated brain areas. Clinical trial registration http://www.chictr.org.cn , identifier ChiCTR2000034862.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.003
GPT teacher head0.213
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations20
Published2022
Admission routes1
Has abstractyes

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