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Record W2055683244 · doi:10.1139/h07-103

Effects of physical training on endothelial function and limb blood flow in type 2 diabetes

2007· review· en· W2055683244 on OpenAlexvenueno aff
Mette P. Sonne, Celena Scheede‐Bergdahl, David B. Olsen, Lise Højbjerre, Amra Ciric Alibegovic, Ninna Bo Nielsen, Bente Stallknecht, Jørn Wulff Helge, Allan Vaag, Flemming Dela

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
FundersLundbeckfondenAase og Ejnar Danielsens FondDanmarks Grundforskningsfond
KeywordsEndothelial dysfunctionVasodilationType 2 diabetesMedicineDiabetes mellitusInternal medicineInsulin resistanceEndotheliumPhysical exerciseConcomitantEndocrinologyCardiology

Abstract

fetched live from OpenAlex

The term "endothelial dysfunction" refers to the inability or attenuated effect of the endothelial cells in participating in the relaxation of the adjacent smooth muscle, thus causing less vasodilation. Although endothelial dysfunction is often seen in patients with type 2 diabetes, it does not necessarily follow that insulin resistance and (or) hyperglycemia is causing the inability to respond properly to vasodilatory stimuli. Rather, this could be related to the impact of concomitant cardiovascular risk factors that are almost invariably present in patients with type 2 diabetes. The impact of physical training - or the opposite, inactivity - on endothelial function is not fully elucidated. Some studies have shown positive effects of physical training, whereas others have not. In general, physical training can improve endothelial function when this is impaired. However, physical training does not seem to have any effect on endothelial function when this is normal.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.002

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 head0.022
GPT teacher head0.296
Teacher spread0.275 · 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 designSystematic review
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

Citations9
Published2007
Admission routes1
Has abstractyes

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