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Record W2735363521 · doi:10.2174/157340207781386684

Arterial Stiffness and Hypertension: A Review of Mechanism and Clinical Relevance

2007· review· en· W2735363521 on OpenAlexaff
Julia Wong

Bibliographic record

VenueCurrent Hypertension Reviews · 2007
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsGreenfield Research (Canada)
Fundersnot available
KeywordsArterial stiffnessMedicineCardiologyBlood pressureInternal medicineDiabetes mellitusArteryEssential hypertensionStiffnessEndocrinology

Abstract

fetched live from OpenAlex

Purpose of review: This review is intended to provide the background for a broad view of the influence of large-artery stiffness on the development of hypertension in aging, diabetes and end-stage renal disease. Recent findings: Arterial stiffness, particularly in aorta, is a major determinant of isolated systolic hypertension in the elderly. Studies have consistently shown that large-artery stiffness results in augmented amplitude of reflected pressure waves and their early return. This disturbed physiological phenomenon can alter the heart-vessel coupling and lead to increased cardiovascular risk. This review describes the structural, functional, environmental and genetic factors that influence arterial stiffness, wave reflection, and blood pressure. It also discusses non-invasive techniques to measure arterial stiffness and analyze arterial waveforms. The effects of various antihypertensive agents with respect to arterial stiffness and blood pressure reduction are examined. In addition, studies on non-pharmacologic interventions to modify large artery behavior are reviewed. Summary: Optimal clinical management of hypertension depends on better understanding of the contribution of vascular stiffness to hypertension. This information has significant implications for therapeutic decisions. Keywords: Arterial stiffness, wave reflections, aging, hypertension

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.214
GPT teacher head0.449
Teacher spread0.235 · 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.

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

Citations3
Published2007
Admission routes1
Has abstractyes

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