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Record W2885767622

Abstract 17925: Physical Exercise Reduces Angiopoietin-like 2 Circulating Levels Only in CAD Patients With Endothelial Dysfunction

2014· article· en· W2885767622 on OpenAlexaboutno aff
CarolYu, DougHayami, MathieuGayda, Jean-FrançoisLarouche, GabrielLapierre, ChristineHenri, JeanLambert, MartinJuneau, JulieLalonge, NathalieThorin-Trescases, AndreArsenault, EricThorin, AnilNigam

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEndothelial dysfunctionOverweightInternal medicineEndotheliumEndocrinologyAngiopoietinCardiologyObesityVEGF receptorsVascular endothelial growth factor
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Angiopoietin-like 2 (angptl2) is a circulating pro-inflammatory and pro-oxidative protein that induces endothelial dysfunction in mice. Plasma angptl2 levels are increased in diabetic or atherosclerotic patients, while a lifestyle intervention has been shown to decrease angptl2 expression in overweight, but otherwise healthy men. Whether angptl2 levels are sensitive to exercise in CAD patients is unknown. Hypothesis: Exercise training reduces angptl2 levels in patients with CAD and endothelial dysfunction. Methods: Stable and optimally treated CAD patients (n=31, 60±2 y/o, 7 females) were enrolled in a 3-month exercise-based prevention program at the Montreal Heart Institute. Blood samples were collected before and at the end of the study to measure plasma levels of angptl2 (ELISA) and of hs-CRP. Endothelial function was assessed by measuring the ratio of the slope of the nuclear tracer Myoview activity-time in the right to the left arm. A ratio lower than 3.55 is indicative of endothelial d...

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 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
Published2014
Admission routes1
Has abstractyes

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