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Record W2161790639 · doi:10.1139/h06-107

Arterial stiffness following repeated Valsalva maneuvers and resistance exercise in young men

2007· article· en· W2161790639 on OpenAlexvenueno aff
Kevin S. Heffernan, Sae Young Jae, David G. Edwards, Erin E. Kelly, Bo Fernhall

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsArterial stiffnessPulse wave velocityBlood pressureCardiologyValsalva maneuverMedicinePulse pressureInternal medicinePulsatile flowPlethysmographRepeated measures designDiastolePeripheralMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to compare arterial stiffness after a bout of resistance exercise (RE) and an experimental condition consisting of repeated Valsalva maneuvers (VMs). Fourteen male participants randomly completed a lower-body, unilateral RE bout and a VM bout designed to alter blood pressure (BP) in a similar pulsatile fashion. Pulse-wave velocity (PWV, measured in metres per second (m.s-1)) was used to measure central and peripheral arterial stiffness and was assessed before and 20 min after each perturbation. Beat-to-beat blood pressure (BP) was assessed during bouts using finger plethysmography. Change in systolic BP, diastolic BP, mean arterial pressure, and pulse pressure were similar during both bouts. Central PWV increased after repeated VMs (7.1 +/- 0.3 m/s to 7.8 +/- 0.3 m/s), but not after RE (7.2 +/- 0.3 m/s to 7.2 +/- 0.3 m/s) (interaction, p = 0.032). There was no change in peripheral PWV after VM (8.9 +/- 0.3 m/s to 9.3 +/- 0.3 m/s) or RE (8.5 +/- 0.2 m/s to 8.4 +/- 0.2 m/s). Arterial stiffness increased after repeated VM. Even though presented with a similar BP load, arterial stiffness did not increase after acute RE. These findings suggest a role for VM in acutely altering arterial properties.

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)0.000
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 score0.977
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.260
Teacher spread0.251 · 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

Citations62
Published2007
Admission routes1
Has abstractyes

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