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Record W3204705082 · doi:10.26689/jcnr.v5i5.2593

Effect of Nursing Intervention on Improving the Cognitive Function of Patients with Mild Cognitive Impairment

2021· article· en· W3204705082 on OpenAlexaboutno aff
Li Sun, Zhenzhu Shang

Bibliographic record

VenueJournal of Clinical and Nursing Research · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionCognitive impairmentMedicineIntervention (counseling)Montreal Cognitive AssessmentQuality of life (healthcare)Physical therapyActivities of daily livingNursingPsychiatry

Abstract

fetched live from OpenAlex

Objective: To explore the effect of nursing intervention on improving the cognitive function of patients with mild cognitive impairment. Methods: Sixty patients with mild cognitive impairment in Weifang Hospital of Traditional Chinese Medicine from January 2020 to January 2021 were randomly selected for this study. They were divided into two groups: a reference group (routine follow-up and daily health education) and a research group (nursing intervention based on the reference group). Results: Before nursing, there was no significant difference in the MoCA, MMSE, ADL, SDS, and SAS scores between the two groups (p > 0.05). After intervention, the MOCA score and MMSE score of the research group were lower than those of the reference group, the ADL score of the research group was higher than that of the reference group, and the quality-of-life score of the research group was also higher than that of the reference group (p < 0.05). Conclusion: Early nursing intervention for patients with mild cognitive impairment can effectively improve their cognitive functions and daily abilities.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.117
GPT teacher head0.466
Teacher spread0.349 · 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 designNon-randomized 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

Citations1
Published2021
Admission routes1
Has abstractyes

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