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Record W7124375562

Practice research on pentatonic scale therapy for mild cognitive impairment elderly in nursing on different physical constitution

2017· article· zh· W7124375562 on OpenAlexaboutno aff
宋艳丽, 刘伟

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languagezh
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)CognitionCognitive impairmentConstitutionScale (ratio)Depression (economics)Vital signs
DOInot available

Abstract

fetched live from OpenAlex

Abstract Objective:To explore the influence of pentatonic scale therapy on elderly people with mild cognitive impairment of different physical conditions.Methods:The study subjects were randomly divided into observation group(31 cases) and control group(29 cases).The patients in observation group adopted pentatonic scale therapy for the different physical constitution based traditional nursing care and treatment of the nursing institution.The patients in control group were treated with routine care and treatment.Montreal Cognitive Assessment(MoCA)and The Geriatric Depression Scale(GDS)were used before and after the intervention to evaluate the two groups.Results:After the intervention,the results showed improvement in somatic selfcare ability,depression,cognitive ability,and other aspects in different degree in two groups(P<0.05).Some of the dimensions reduced on the followup interview 2 weeks after intervention,the intervention effect was statistically significant different (P<0.05).Conclusions:Pentatonic scale therapy on different physical constitution could improve efficiency on physical selfmaintenance,depression,and cognitive ability of the elderly with mild cognitive impairment.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.488
GPT teacher head0.648
Teacher spread0.160 · 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 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
Published2017
Admission routes1
Has abstractyes

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