MétaCan
Menu
Back to cohort
Record W1916647303 · doi:10.1590/1414-431x20153904

Effects of exercise and metformin on the prevention of glucose intolerance: a comparative study

2015· article· en· W1916647303 on OpenAlexafffund
Carlos Alexandre Molena Fernandes, Ciomar Aparecida Bersani‐Amado, Zachary M. Ferraro, Luzia Jaeger Hintze, Nélson Nardo, Roberto Kenji Nakamura Cuman

Bibliographic record

VenueBrazilian Journal of Medical and Biological Research · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health Research
KeywordsMetforminDexamethasoneInternal medicineMedicineEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

We aimed to evaluate the effects of aerobic exercise training (4 days) and metformin exposure on acute glucose intolerance after dexamethasone treatment in rats. Forty-two adult male Wistar rats (8 weeks old) were divided randomly into four groups: sedentary control (SCT), sedentary dexamethasone-treated (SDX), training dexamethasone-treated (DPE), and dexamethasone and metformin treated group (DMT). Glucose tolerance tests and in situ liver perfusion were undertaken on fasting rats to obtain glucose profiles. The DPE group displayed a significant decrease in glucose values compared with the SDX group. Average glucose levels in the DPE group did not differ from those of the DMT group, so we suggest that exercise training corrects dexamethasone-induced glucose intolerance and improves glucose profiles in a similar manner to that observed with metformin. These data suggest that exercise may prevent the development of glucose intolerance induced by dexamethasone in rats to a similar magnitude to that observed after metformin treatment.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.188
GPT teacher head0.452
Teacher spread0.264 · 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 designNon-randomized trial
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

Citations5
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBrazilian Journal of Medical and Biological ResearchSame topicDiet and metabolism studiesFrench-language works237,207