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Record W7052311323

Quality of Life in Patients with Type 2 Diabetes Mellitus

2016· other· en· W7052311323 on OpenAlexaboutno aff

Bibliographic record

Venuezvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus) · 2016
Typeother
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Type 2 Diabetes MellitusCognitionMontreal Cognitive AssessmentDiabetes mellitusCognitive impairmentType 2 diabetes
DOInot available

Abstract

fetched live from OpenAlex

© 2016, Springer Science+Business Media New York.The article presents the results of a survey which included 120 patients with type 2 diabetes mellitus (DM-2) without complications, without stroke in anamnesis, with no active complaints about loss of memory, and/or other cognitive functions. The assessment tools for cognitive functions were MMSE, MoCA test, and TMT (parts A and B). SF-36 test was used to assess the quality of life (QoL). It was revealed that the QoL in patients with type 2 diabetes was significantly lower than in the general population. The correlation between QoL and the duration of the disease, the degree of cognitive impairment, and emotional state of patients was evaluated. It was revealed that QoL indicators significantly reduced with the growth of cognitive impairment and emotional disorders. The most significant reduction is revealed in the role functioning indexes and role-emotional functioning indexes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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
Published2016
Admission routes1
Has abstractyes

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