MétaCan
Menu
Back to cohort
Record W2117270530 · doi:10.2174/1874192400802010003

Acute and Chronic Impact of Dynamic Exercise on Arterial Stiffness in Older Hypertensives

2008· article· en· W2117270530 on OpenAlexafffund
Kunihiko Aizawa, Robert J. Petrella

Bibliographic record

VenueThe Open Cardiovascular Medicine Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsWestern UniversityParkwood InstituteLawson Health Research Institute
FundersLawson Health Research Institute
KeywordsMedicineArterial stiffnessAerobic exerciseCardiologyInternal medicinePhysical therapyPulse wave velocityBlood pressure

Abstract

fetched live from OpenAlex

Arterial stiffness increases with ageing and hypertension. Regular physical activity has been recommended as an important management component of hypertension. The purpose of this study was to examine the acute impact of maximal dynamic exercise and the effect of 20 weeks of aerobic exercise on arterial stiffness of the carotid and brachial arteries in older hypertensives. Nine previously sedentary and treated older hypertensives (2 men and 7 women, age 68.2 +/- 5.4 yrs) performed maximal treadmill exercise to volitional fatigue while arterial stiffness indices (arterial distensibility and beta stiffness index) were measured prior to, immediately (about 10 min) following, and 24 h following maximal exercise. These measurements were repeated following 20 weeks of moderate intensity aerobic exercise training. Maximal exercise had no impact on arterial stiffness indices immediately and 24 h following exercise intervention. Following 20 weeks of training, arterial stiffness indices remained unchanged at rest and following maximal exercise. These data show that, in older hypertensives, 1) acute maximal dynamic exercise had no impact on arterial stiffness of the carotid and brachial arteries, and 2) 20 weeks of moderate intensity aerobic exercise training failed to modify arterial stiffness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.020
GPT teacher head0.318
Teacher spread0.298 · 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 teacher head, 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

Citations37
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueThe Open Cardiovascular Medicine JournalSame topicCardiovascular Health and Disease PreventionFrench-language works237,207