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Lack of Circadian Variation of Pulse Wave Velocity Measurements in Healthy Volunteers

2010· article· en· W2004877349 on OpenAlexaff
Luciano F. Drager, Luzia Diegues‐Silva, Patricia Martini Diniz, Geraldo Lorenzi‐Filho, Eduardo Moacyr Krieger, Luiz Aparecido Bortolotto

Bibliographic record

VenueJournal of Clinical Hypertension · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsHypertension Canada
Fundersnot available
KeywordsPulse wave velocityMedicineBlood pressureArterial stiffnessCircadian rhythmInternal medicineCardiologyNoonDiastolePulse pressureEndocrinologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Arterial stiffness is an independent marker of cardiovascular events. Pulse wave velocity (PWV) is a validated method to detect arterial stiffness that can be influenced by several factors including age and blood pressure. However, it is not clear whether PWV could be influenced by circadian variations. In the present study, the authors measured blood pressure and carotid-femoral PWV measurements in 15 young healthy volunteers in 4 distinct periods: 8 am, noon, 4 pm, and 8 pm. No significant variations of systolic (P=.92), mean (P=.77), and diastolic (P=.66) blood pressure among 8 am (113±15, 84±8, 69±6 mm Hg), noon (114±13, 83±8, 68±6 mm Hg), 4 pm (114±13, 85±8, 70±7 mm Hg), and 8 pm (113±7, 83±10, 68±7 mm Hg), respectively, were observed. Similarly, carotid-femoral PWV did not change among the periods (8 am: 7.6 ± 1.4 m/s, noon: 7.4±1.1 m/s, 4 pm: 7.6±1.0 m/s, 8 pm, 7.6±1.3 m/s; P=.85). Considering all measurements, mean blood pressure significantly correlated with PWV (r=.31; P=.016). In young healthy volunteers, there is no significant circadian variation of carotid-femoral PWV. These findings support the concept that it does not appear mandatory to perform PWV measurements at exactly the same period of the day.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.344
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
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 integrity0.0000.001
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 head0.189
GPT teacher head0.412
Teacher spread0.223 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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