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Record W2137680960 · doi:10.1155/2013/285948

Potential Mechanisms of Exercise in Gestational Diabetes

2013· article· en· W2137680960 on OpenAlexaff
Saeid Golbidi, Ismail Laher

Bibliographic record

VenueJournal of Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGestational diabetesAdipokineMedicinePregnancyInsulin resistanceOxidative stressDiabetes mellitusEndocrinologyInternal medicineAdipose tissueBioinformaticsPhysiologyGestationBiology

Abstract

fetched live from OpenAlex

Gestational diabetes mellitus (GDM) is defined as glucose intolerance first diagnosed during pregnancy. This condition shares same array of underlying abnormalities as occurs in diabetes outside of pregnancy, for example, genetic and environmental causes. However, the role of a sedentary lifestyle and/or excess energy intake is more prominent in GDM. Physically active women are less likely to develop GDM and other pregnancy-related diseases. Weight gain in pregnancy causes increased release of adipokines from adipose tissue; many adipokines increase oxidative stress and insulin resistance. Increased intramyocellular lipids also increase cellular oxidative stress with subsequent generation of reactive oxygen species. A well-planned program of exercise is an important component of a healthy lifestyle and, in spite of old myths, is also recommended during pregnancy. This paper briefly reviews the role of adipokines in gestational diabetes and attempts to shed some light on the mechanisms by which exercise can be beneficial as an adjuvant therapy in GDM. In this regard, we discuss the mechanisms by which exercise increases insulin sensitivity, changes adipokine profile levels, and boosts antioxidant mechanisms.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.257
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2013
Admission routes1
Has abstractyes

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