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Record W2121886301 · doi:10.1152/jappl.2001.91.6.2531

Left ventricular wave speed

2001· article· en· W2121886301 on OpenAlexaff
Jiun‐Jr Wang, Kim H. Parker, John V. Tyberg

Bibliographic record

VenueJournal of Applied Physiology · 2001
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsApex (geometry)DiastolePhysicsSystoleConstrictionBlood pressureMathematicsAnatomyCardiologyInternal medicineGeometryMedicine

Abstract

fetched live from OpenAlex

Left ventricular (LV) wave speed (LVWS) was studied experimentally and confirmed in theory. Combining the definition of elastance (E) with the equations for the conservation of mass and momentum shows that LVWS is proportional to the square root of ELA, where L is long-axis length and A is the cross-sectional area, and the density of the blood. (We defined ELA = gamma, where gamma is compressibility.) We studied nine open chest, anesthetized dogs, three of which were studied during caval constriction when LV end-diastolic pressure was < or =0 mmHg. The hearts were paced at approximately 90 beats/min, and LV cross-sectional area was measured by using two pairs of ultrasonic crystals; E was calculated from the LV pressure-area loop. A pulse generator was connected to the LV apex, and LVWS was measured by using two pressure transducers: one near the apex and the other near the base. Their distance was measured roentgenographically and compared with the diameter of a reference ball. LVWS ranged from approximately 1 m/s during diastole to approximately 10 m/s during systole. The slope of the log c (where c is wave speed) vs. log gamma was 0.546, which is in agreement with theory (0.5). When gamma < or = 0, LVWS was approximately 1.5 m/s.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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 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

Citations17
Published2001
Admission routes1
Has abstractyes

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