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Record W2370984236

The Related Factors Analysis of Elderly People with Mild Cognitive Impairment in Shanghai Community

2013· article· en· W2370984236 on OpenAlexaboutno aff
Sun Xi-rong

Bibliographic record

VenueMedical Innovation of China · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitionPersonalityMontreal Cognitive AssessmentCognitive impairmentPopulationGerontologyAudiologyClinical psychologyPsychiatryEnvironmental healthPsychology
DOInot available

Abstract

fetched live from OpenAlex

Objective:To investigate the elderly people with mild cognitive impairment(MCI)characteristics and related factors in Shanghai community. Method:Used a case-control study method,selected 174 cases as MCI group,another 174 patients with normal cognitive function as normal control group from the 55 years of age or older with mild cognitive dysfunction in the city of Shanghai Pudong New Area,used MoCA on human cognitive function was assessed in the degree of education,personality tendency,behavioral factors,the dietary habits and other related factors analysis at the same time. Result:The analysis showed that overall cognitive function cognitive function was significant difference compared with different areas(P 0.05);in the multiple factors,compared with personality tendency,behavioral factors,dietary habits as well as the population level of education was significantly associated with MCI. Conclusion:The patients with mild cognitive impairment of memory,visual spatial ability,executive ability,language function, calculation and attention have different degrees of injury. Patients with introverted,yummy dinner and drinking are independent risk factors of MCI,while the use of people’s education level and regular exercise habits are protective factors of MCI.

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)0.001
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 score0.103
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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