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Record W2883691008 · doi:10.14744/semb.2018.37929

The relationship between polyneuropathy and cognitive functions in type 2 Diabetes Mellitus patients

2018· article· en· W2883691008 on OpenAlexaboutno aff
Sibel Mumcu Timer

Bibliographic record

VenueSiSli Etfal Hastanesi Tip Bulteni / The Medical Bulletin of Sisli Hospital · 2018
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPolyneuropathyMedicineDiabetes mellitusInternal medicineMontreal Cognitive AssessmentDementiaRisk factorCoronary artery diseaseType 2 diabetesDiseasePhysical therapyEndocrinology

Abstract

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OBJECTIVES: Type 2 Diabetes Mellitus (DM) is a risk factor for mild cognitive impairment (MCI), Alzheimer's disease and vascular dementia. However, it is not known which pathophysiological mechanisms lead to impairment in cognitive functions in Type 2 DM. This study aims to compare the cognitive functions of diabetic patients with and without polyneuropathy using standardized Mini-Mental Test (MMSE) and the Montreal Cognitive Assessment Scale (MoCA) and to assess whether the presence of polyneuropathy is a predictive factor for the development of cognitive impairment. METHODS: Patients with DM who underwent our EMG laboratory for polyneuropathy between January 2014 and January 2015 were included in this study. Patients who underwent electrophysiological examinations were evaluated for polyneuropathy. Patients with polyneuropathy were classified as a patient group and other patients as a control group. In all cases, MMSE and MoCA were administered. The demographic data and educational status of the patients were recorded. Hypertension, coronary artery disease, smoking and alcohol use were questioned. Their complaints, duration of illness and the treatment they were receiving were questioned. Glycosylated hemoglobin (HBA1C) values in the last three months and physical examination findings of patients were recorded. Patients with and without polyneuropathy were compared with statistical methods. RESULTS: Polyneuropathy was detected in 34 (42%) of the 81 patients who participated in our study. The age, disease duration and HBA1C levels were statistically higher in the polyneuropathy group than in the control group (p=0.024, p=0.000, p=0.016). However, there was no statistically significant difference between MMSE and MoCA scores of these groups. In both groups, there were no patients scoring below the MMSE cut-off value of 24. Seventeen of the 34 patients (50%) in the polyneuropathic group and 19 (40,4%) of the 47 patients in the control group had scores below the MoCA cut-off value 21. However, there was no statistically significant difference between the two groups. We also found that the mean MoCA value of all DM patients was 21, which was the MoCA cut-off value. Also, factors affecting cognitive functions in all Type 2 DM patients were evaluated by logistic regression analysis, and it was found that duration of education was an independent factor affecting cognitive impairment (OR=8.167; p=0.001). CONCLUSION: In our study, we did not observe significant differences between MMSE and MoCA scores of Type 2 DM patients with and without polyneuropathy. However, the cross-sectional nature of our study makes it impossible to comment on this issue. To clarify whether the presence of polyneuropathy is a predictive factor in the development of cognitive impairment in Type 2 DM, there is a need for a larger sample group and long-term follow-up studies. It has also been shown that patients with Type 2 DM may have low scores according to the MOBID cut-off value even though peripheral neurologic involvement findings are not observed. In the Type 2 DM population, it has also been shown that MoCA may be affected by education level.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.034
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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".

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Citations5
Published2018
Admission routes1
Has abstractyes

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