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Record W2729338719 · doi:10.1152/advan.00190.2016

Cardiopulmonary physiology: why the heart and lungs are inextricably linked

2017· review· en· W2729338719 on OpenAlexaff
Kevin Verhoeff, Jamie R. Mitchell

Bibliographic record

VenueAJP Advances in Physiology Education · 2017
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPreloadAfterloadCardiologyVenous return curveMedicineCardiac outputInternal medicineDiastoleVascular resistanceVentilation (architecture)HemodynamicsBlood pressurePhysics

Abstract

fetched live from OpenAlex

Because the heart and lungs are confined within the thoracic cavity, understanding their interactions is integral for studying each system. Such interactions include changes in external constraint to the heart, blood volume redistribution (venous return), direct ventricular interaction (DVI), and left ventricular (LV) afterload. During mechanical ventilation, these interactions can be amplified and result in reduced cardiac output. For example, increased intrathoracic pressure associated with mechanical ventilation can increase external constraint and limit ventricular diastolic filling and, therefore, output. Similarly, high intrathoracic pressures can alter blood volume distribution and limit diastolic filling of both ventricles while concomitantly increasing pulmonary vascular resistance, leading to increased DVI, which may further limit LV filling. While LV afterload is generally considered to decrease with increased intrathoracic pressure, the question arises if the reduced LV afterload is primarily a consequence of a reduced LV preload. A thorough understanding of the interaction between the heart and lungs can be complicated but is essential for clinicians and health science students alike. In this teaching review, we have attempted to highlight the present understanding of certain salient aspects of cardiopulmonary physiology and pathophysiology, as well as provide a resource for multidisciplined health science educators and students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.055
GPT teacher head0.400
Teacher spread0.344 · 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 designOther design
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

Citations67
Published2017
Admission routes1
Has abstractyes

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