MétaCan
Menu
Back to cohort
Record W2044460082 · doi:10.1139/h07-031

The effect of exercise, training, and inactivity on insulin sensitivity in diabetics and their relatives: what is new?

2007· review· en· W2044460082 on OpenAlexvenueno aff
Torben Østergård, Niels Jessen, Ole Schmitz, Lawrence J. Mandarino

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsInsulin resistanceType 2 diabetesInternal medicineMedicineEndocrinologyDiabetes mellitusAerobic exerciseInsulinObesitySkeletal muscleInsulin sensitivityPhysical exercise

Abstract

fetched live from OpenAlex

Insulin resistance is a hallmark characteristic of type 2 diabetes. However, in healthy first-degree relatives of type 2 diabetics, insulin resistance is often present years before glucose intolerance or diabetes becomes clinically manifest. The mechanisms of insulin resistance involve conditions leading to an increased supply of fatty acids (e.g., high energy intake, obesity) and conditions in which the degradation/oxidation of muscular fatty acids is impaired. Several large-scale studies have documented the fact that increased physical activity can reduce or at least postpone the development of type 2 diabetes, and low physical fitness is a clear independent risk factor for the development of type 2 diabetes. The mechanisms responsible for the improvement in insulin sensitivity after exercise training have been studied extensively, but are not fully understood. This review focuses on insulin resistance in skeletal muscle and, in particular, its relation to changes in aerobic fitness in type 2 diabetics and their first-degree relatives.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.313
Teacher spread0.282 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physiology Nutrition and MetabolismSame topicAdipose Tissue and MetabolismFrench-language works237,207