MétaCan
Menu
← Back to cohort
Record W2398411424

[Effect of type 2 diabetes with hypertension on cognitive function-a study on elderly living in the communities of Beijing].

2014· article· en· W2398411424 on OpenAlexaboutno aff
Xin Gao, Chunbo Duan, Li Bao, Huiyan Yu, Bin Qin, Ruomei Qi, Fangkun Gao

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineBeijingCognitionInternal medicineType 2 Diabetes MellitusLogistic regressionDiabetes mellitusType 2 diabetesCognitive impairmentPhysical therapyPsychiatryEndocrinologyChina
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the effects of type 2 diabetes (T2DM) with hypertension on cognitive function in those community-based elderly who were aged 60 and over, in Beijing. METHODS: 82 patients with T2DM, 142 patients with both T2DM and hypertension and 277 normal controls were investigated in this study. Both methods as: the Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Examination (MMSE)were used to determine cognitive change. RESULTS: The total MMSE scores showed significant decrease between T2DM with hypertension and controls [(28.42 ± 1.52) vs. (28.88 ± 1.47), P < 0.05]. The MoCA score of the total scores [(25.20 ± 3.91) vs. (26.50 ± 3.29), P < 0.05], sub-scores of visuospatial, executive [(3.60 ± 1.56) vs. (3.96 ± 1.18), P < 0.05] and language [(2.10 ± 0.80) vs. (2.37 ± 0.80), P < 0.05] significantly decreased in T2DM patients with hypertension and in the normal controls. Data from the Multiple logistic regression analysis showed that older age and less education were risk factors for cognitive impairment. CONCLUSION: T2DM and hypertension damaged the cognitive function of patients.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→French-language works237,207→