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The Arterial Pulse Wave and Vascular Compliance

2009· review· en· W2050691216 on OpenAlexaff
Joseph P. Noon

Bibliographic record

VenueProgress in Cardiovascular Nursing · 2009
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsCARE Canada
Fundersnot available
KeywordsMedicineCompliance (psychology)Pulse (music)Pulse waveCardiologyOptics

Abstract

fetched live from OpenAlex

For just over 1 century, we have relied on cuff sphygmomanometry to measure blood pressure at a peripheral (brachial) site. This measurement provides a quantitative snapshot of hemodynamic activity at 1 part of the arterial tree. Because the heart and brain are exposed to central (aortic) and not peripheral (brachial) pressure, it might be timely for nurses to start looking at alternative techniques to provide more meaningful information on central hemodynamics. The noninvasive technique of applanation tonometry allows such measurements to be performed quickly in the nursing clinic. By analyzing the pulse wave and calculating pulse wave velocity, the technique also assesses arterial "stiffness." This method of cardiovascular assessment further enables nurses to monitor the central effects of antihypertensive, lipid lowering, and other drug therapy over time.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.004

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 head0.064
GPT teacher head0.371
Teacher spread0.307 · 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 designNot applicable
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

Citations1
Published2009
Admission routes1
Has abstractyes

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