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Olfactory dysfunction is a risk factor for the comorbidity of mild cognitive impairment and Type 2 diabetes mellitus.

2021· article· en· W3204592881 on OpenAlexaboutno aff
Lina Gong, Jianfei Xie, Jia Liu, Guanxiu Tang, Wanli Lin, Zhen Zhang

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComorbidityMontreal Cognitive AssessmentInternal medicineMedicineLogistic regressionType 2 Diabetes MellitusDiabetes mellitusCognitionRisk factorOlfactory systemPsychiatryDementiaDiseaseEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVES: Diabetes can accelerate cognitive decline and hence affect the prognosis of patients with Type 2 diabetes mellitus (T2DM). Olfactory assessment can facilitate the early identification of cognitive impairment among T2DM patients. This study aims to evaluate the effects of olfactory function on mild cognitive impairment (MCI) in patients with T2DM. METHODS: test, and multivariable logistic regression was used to determine the relevant factors contributing to the comorbidity of MCI and T2DM. RESULTS: <0.05]. The number of patients with olfactory dysfunction also differed significantly between the 2 groups (120 vs 50). After adjustment for age, educational level, T2DM duration, fasting insulin, and glycosylated hemoglobin (HbA1c), multivariate logistic regression analysis showed older age (OR=1.14, 95% CI 1.09 to 1.20), longer course of diabetes (OR=1.21, 95% CI 1.12 to 1.31), and olfactory-impaired (OR=4.61, 95% CI 3.04 to 6.18) were independent risk factors for T2DM combined with MCI, and the high education level (OR=0.26, 95% CI 0.15 to 0.38) was an independent protective factor for T2DM combined with MCI. CONCLUSIONS: Olfactory dysfunction is an independent risk factor for the comorbidity of MCI and T2DM. Special attention should be paid to those with olfactory dysfunction when carrying out cognitive interventions in T2DM 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.153
GPT teacher head0.261
Teacher spread0.107 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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