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Is metformin the only culprit for cognitive impairment in diabetes?

2023· preprint· en· W4381546514 on OpenAlexaboutno aff
R Murali, Archith Boloor, H Yeshwanth

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMetforminMedicineDiabetes mellitusType 2 diabetesCulpritInternal medicineMontreal Cognitive AssessmentCognitionCognitive impairmentEndocrinologyPsychiatryDisease

Abstract

fetched live from OpenAlex

<ns5:p> <ns5:bold>Background:</ns5:bold> As patients with diabetes are conventionally on a long-term prescription for metformin, it is important to identify any increase in their risk for developing cognitive disorders due to metformin. Hence, an attempt was made to study the cognitive impairment by using Montreal Cognitive Assessment test (MoCA) as a possible predictor of development of cognitive impairment in type 2 diabetes patients on metformin therapy. </ns5:p> <ns5:p> <ns5:bold>Methods:</ns5:bold> Four hundred type 2 diabetes patients on metformin were enrolled for this cross-sectional study, and data recorded. Cognitive test MoCA was administered and a score less than 26 was considered abnormal. </ns5:p> <ns5:p> <ns5:bold>Results:</ns5:bold> In this study, the participants on metformin had a statistically significant correlation with age &gt; 65 years, duration of diabetes (&gt;5 years), metformin dose (1 gm and more) and presence of diabetes complications. Ordinal regressions showed significant correlation between abnormal MoCA scores and older age, longer duration of DM, and presence of one of the DM complications. </ns5:p> <ns5:p> <ns5:bold>Conclusions:</ns5:bold> Amongst patients receiving medical therapy for control of type 2 diabetes, participants using metformin showed a very high prevalence rate of abnormal MoCA scores (85%). Increased duration of metformin intake leads to a decline in MoCA performance. </ns5:p>

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.111
GPT teacher head0.409
Teacher spread0.298 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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