MétaCan
Menu
Back to cohort

Morphometric characteristics of the brain in patients with type 1 diabetes mellitus on diff erent modes of basic bolus insulin therapy

2020· article· en· W3097330221 on OpenAlexaboutno aff
Yu. G. Samoilova, М.В. Матвеева, О. С. Тонких, O. P. Leiman, N. Yu. Fimushkina, D. A. Kudlai, I. N. Vorzhtsova I.N, M. I. Kharakhulakh

Bibliographic record

VenueClinical Medicine (Russian Journal) · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycated hemoglobinInsulinDiabetes mellitusType 1 diabetesDiabetic ketoacidosisKetoacidosisType 2 Diabetes MellitusInsulin pumpMagnetic resonance imagingBolus (digestion)Type 2 diabetesInternal medicineSurgeryEndocrinologyRadiology

Abstract

fetched live from OpenAlex

Objective— to study the morphometric characteristics of the brain in patients with type 1 diabetes mellitus (DM) receiving insulin therapy in diff erent modes, taking into account the variability of glycemia. Material and methods.120 patients with type 1 diabetes, living in Tomsk and the Tomsk Region, were examined. All patients were divided into 2 groups: group 1 — patients receiving insulin in the base-bolus regimen of multiple insulin injections (MII), group 2 — using pump insulin therapy by continuous subcutaneous infusion of insulin using a wearable dispenser (CSII). Patients took this therapy for at least 6 months before inclusion in the study. All patients underwent a general clinical examination, testing of cognitive functions using the Montreal scale (MoCA test), continuous monitoring of blood glycemia (CMG) using iPro™ 2 Professional Continuous Glucose Monitoring (Medtronic, USA), FreeStyle Libre (Abbot, USA) in for 14 days, standard magnetic resonance imaging (MRI) on a 1.5 Tesla apparatus in axial, sagittal and coronal projections using T2, TE, T1, and using programs that suppress the signal of free water. We processed the results of MRI using Free Surfer (USA) and recon-all segmentation algorithm. Statistical analysis was performed using the R-system software package. Results.It was found that in both groups with type 1 diabetes there was a decrease in cognitive functions. It has been shown that CSII is associated with the best completion of the MoCA test. In addition, it has been reported that more frequent episodes of diabetic ketoacidosis and increased glycated hemoglobin (HbA1c) are the main causes of cognitive impairment in this group of patients. Changes in the morphometric parameters of the brain are interconnected with glycemic variability. Conclusion.In patients with type 1 diabetes, cognitive impairment associated with acute and chronic hyperglycemia was verifi ed. Morphometric features of brain changes are more dependent on glycemic variability. CSII helps improve cognitive function.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.328
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueClinical Medicine (Russian Journal)Same topicDiabetes Management and ResearchFrench-language works237,207