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Record W2129792557 · doi:10.1113/jphysiol.2014.272963

Genesis of the characteristic pulmonary venous pressure waveform as described by the reservoir‐wave model

2014· article· en· W2129792557 on OpenAlexafffund
J. Christopher Bouwmeester, Israel Belenkie, Nigel G. Shrive, John V. Tyberg

Bibliographic record

VenueThe Journal of Physiology · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchBristol-Myers Squibb CanadaBristol-Myers Squibb
KeywordsVentricleCardiac cycleCardiologyDiastoleMechanicsInternal medicineWaveformBackflowBlood pressureGeologyMedicinePhysics

Abstract

fetched live from OpenAlex

Key points In pressure flow data from a pulmonary vein, we use the reservoir wave model to separate the effects of an elastic venous reservoir from the effects of waves created by the heart. Wave intensity analysis was used to separate the effects of waves generated upstream by the right ventricle from the effects of waves generated downstream by the left atrium and left ventricle. Most waves are created by the left atrium and left ventricle and can be linked to events that occur during the cardiac cycle. Waves transmitted through the pulmonary circulation are attenuated less when blood volume is increased but are attenuated more and delayed when the lungs are expanded. The drainage of pulmonary arterial and venous reservoirs are responsible for substantial changes in measured pulmonary venous pressure and flow but waves are associated with the conventional landmarks of these characteristic waveforms. Abstract Conventional haemodynamic analysis of pulmonary venous and left atrial (LA) pressure waveforms yields substantial forward and backward waves throughout the cardiac cycle; the reservoir wave model provides an alternative analysis with minimal waves during diastole. Pressure and flow in a single pulmonary vein (PV) and the main pulmonary artery (PA) were measured in anaesthetized dogs and the effects of hypoxia and nitric oxide, volume loading, and positive‐end expiratory pressure (PEEP) were observed. The reservoir wave model was used to determine the reservoir contribution to PV pressure and flow. Subtracting reservoir pressure and flow resulted in ‘excess’ quantities which were treated as wave‐related. Wave intensity analysis of excess pressure and flow quantified the contributions of waves originating upstream (from the PA) and downstream (from the LA and/or left ventricle (LV)). Major features of the characteristic PV waveform are caused by sequential LA and LV contraction and relaxation creating backward compression (i.e. pressure‐increasing) waves followed by decompression (i.e. pressure‐decreasing) waves. Mitral valve opening is linked to a backwards decompression wave (i.e. diastolic suction). During late systole and early diastole, forward waves originating in the PA are significant. These waves were attenuated less with volume loading and delayed with PEEP. The reservoir wave model shows that the forward and backward waves are negligible during LV diastasis and that the changes in pressure and flow can be accounted for by the discharge of upstream reservoirs. In sharp contrast, conventional analysis posits forward and backward waves such that much of the energy of the forward wave is opposed by the backward wave.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.229
Teacher spread0.212 · 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 designBench or experimental
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

Citations11
Published2014
Admission routes2
Has abstractyes

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