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Record W1864647398 · doi:10.14341/dm2014283-90

Analysis of the Role of Neurospecific Proteins in the Diagnosis of Cognitive Dysfunction in Patients with Type 1 Diabetes Mellitus

2014· article· en· W1864647398 on OpenAlexaboutno aff
Yulia Samoylova, М. В. Новоселова, Natalya Grigor'evna Zhukova, О. С. Тонких

Bibliographic record

VenueDiabetes Mellitus · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentCognitionMagnetic resonance imagingDiabetes mellitusGlial fibrillary acidic proteinCognitive impairmentInternal medicineCentral nervous systemCognitive deficitPathologyEndocrinologyPsychiatryRadiologyImmunohistochemistry

Abstract

fetched live from OpenAlex

Background. Impairment of the central nervous system manifested as cognitive dysfunction caused by metabolic or structural changes is a severe progressive vascular complication of type 1 diabetes mellitus (T1DM). Significant difficulties in the diagnosis of cognitive dysfunction are associated with subjective diagnostic techniques. Objective. To identify the role of neurospecific markers in the diagnosis of cognitive dysfunction in patients with T1DM. Materials and Methods. A total of 58 patients with T1DM aged 16?30 years were included in this study. The control group included 29 healthy young adults matched by gender and age. The survey included clinical and laboratory examinations, psychological testing and magnetic resonance imaging (MRI) of the brain. The Montreal Cognitive Assessment (MoCA) was used to screen for cognitive impairment. The levels of neurospecific proteins (S100, glial fibrillary acidic protein and myelin basic protein) were determined to identify early markers of cognitive impairment. MRI of the brain was performed using a Siemens Magnetom 1.0 T system to assess structural changes in the central nervous system. Results. The study revealed increased levels of all neurospecific proteins, which correlated with parameters of hyperglycaemia and cognitive deficit (MoCA scores of

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 score0.095
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.012
GPT teacher head0.210
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 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

Citations3
Published2014
Admission routes1
Has abstractyes

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