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Association of Serum BDNF with Severity of Cognitive Disorders in Patients with Type 2 Diabetes

2022· article· en· W4309206981 on OpenAlexaboutno aff
Irina V. Gatckikh

Bibliographic record

VenuePersonalized Psychiatry and Neurology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyCognitionMontreal Cognitive AssessmentMedicineDepression (economics)Internal medicineHospital Anxiety and Depression ScaleDiabetes mellitusType 2 Diabetes MellitusType 2 diabetesPsychologyClinical psychologyPsychiatryCognitive impairmentEndocrinology

Abstract

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Background: Cognitive disorders are common in patients with type 2 diabetes mellitus (DM2) and affect the quality of life, work and social adaptation. Diagnosis of cognitive disorders is carried out using various tests, each of which has its own advantages and disadvantages. Aim: To study of the association between serum level of BDNF and the severity of cognitive disorders in patients with DM2. Materials and methods: Included in the study 61 patients with DM2 complicated by central neuropathy with cognitive disorders and 28 clinically healthy volunteers without DM2. The cognitive and depressive disorders were evaluated using the Montreal Cognitive Assessment (MoCA), Frontal Assessment Batter (FAB), Hospital Anxiety and Depression Scale (HADS). The serum level of BDNF was determined via the method of enzyme-linked immunosorbent assay ac[1]cording. Results: Cognitive disorders in patients with DM2 manifests in the form of disorders of spatial orientation, attention and short-term memory. Frontal dysfunction, mainly in the form of impaired conceptualization and grasping reflexes, was recorded in 30% of patients with DM2. The serum level of BDNF in patients with DM2 is significantly lower than in healthy volunteers and is associated with the duration of DM2, the serum level of HbA1c. Conclusion: Serum level of BDNF may by potential biochemical marker of metabolic cognitive disorders in DM2.

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 score0.008
Threshold uncertainty score0.379

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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