MétaCan
Menu
Back to cohort
Record W2368691175

Study on cognitive dysfunction and analysis of risk factor of patients with type-IIdiabetes mellitus

2014· article· en· W2368691175 on OpenAlexaboutno aff
Wang Kai-lian

Bibliographic record

VenueJournal of Harbin University of Commerce · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsInternal medicineMedicineDiabetes mellitusPostprandialBlood pressureMontreal Cognitive AssessmentRisk factorEndocrinologyBlood sugarType 2 Diabetes MellitusDiabetic nephropathyCholesterolUric acidCreatinineDementiaDisease
DOInot available

Abstract

fetched live from OpenAlex

To investigate the risk factors of mild cognitive impairment(MCI) in patients with type Ⅱ diabetes(T2 DM),a total of 165 T2 DM patients were divided into T2 DM with MCI group(n = 95) and T2 DM with normal cognitive function(NMCI) group(n = 70). Montreal cognitive assessment scale(MoCA) was used to assess the functional status in two groups of patients. Non condition logstic regression was used to analyze the related factors of cognitive dysfunction. Compared with the control group,the diabetes course,blood levels of HbAIc,fasting insulin,total cholesterol,low-density lipoprotein-cholesterol,homocysteic acid,and micro urine protein significantly increased. There were no significant differences in BMI,fasting blood glucose,postprandial 2 h blood sugar,triglycerides,high density lipoprotein-cholesterol,creatinine,blood uric acid,contractive pressure,and diastolic blood pressure between the two groups. Multiple regression analysis showed that older age,inefficient control of blood glucose,long duration of diabetes mellitus,history of hypertension,diabetic nephropathy,and diabetic perineuropathy were significantly independent determinant for the T2 DM with cognitive dysfunction. Many risk factors may play a part in T2 DM with MCI. Early detection and prompting medical attention may help prevent and decrease the prevalence of MCI in patients with T2 DM.

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)0.000
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 score0.004
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Harbin University of CommerceSame topicNeurological Disease Mechanisms and TreatmentsFrench-language works237,207