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Periodicity, time constants of drainage, and the mechanical determinants of peak cardiac output during exercise

2019· article· en· W2968048346 on OpenAlexaff
Sheldon Magder, Gabriel Famulari, Brian Gariepy

Bibliographic record

VenueJournal of Applied Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCardiac outputCardiologyInternal medicinePhysical exerciseMedicineHemodynamics

Abstract

fetched live from OpenAlex

To analyze mechanical adaptations that must occur in the cardiovascular system to reach the high cardiac outputs known to occur at peak aerobic performance, we adapted a computational model of the circulation by adding a second parallel venous compartment as proposed by August Krogh in 1912. One venous compartment has a large compliance and slow time constant of emptying; it is representative of the splanchnic circulation. The other has a low compliance and fast time constant of emptying and is representative of muscle beds. Fractional distribution between the two compartments is an important determinant of cardiac output. Parameters in the model were based on values from animal and human studies normalized to a 70 kg male. The baseline cardiac output was set at 5 L/min, and we aimed for 25 L/min at peak exercise with a fractional flow to the peripheral-muscle region of 90%. Finally, we added the equivalent of a muscle pump. Adjustments in circuit and cardiac parameters alone increased cardiac output to only 15.6 L/min because volume accumulated in the muscle compartment and limited a higher cardiac output. Addition of muscle contractions decompressed the muscle region and allowed cardiac output to increase to 23.4 L/min. The pulsatility of blood flow imposes important constraints on the adaptations of cardiac and circulatory functions because it fixes the times for filling and emptying. Flow is further limited by the limits of cardiac filling on each beat. Muscle contractions play a key role by decompressing volume that would otherwise accumulate in the muscle vasculature and by decreasing the time for stroke return to the right ventricle. NEW & NOTEWORTHY We used a computational model of the circulation and previous human and animal data to model mechanical changes in the heart and circulation that are needed to reach the known high cardiac output at peak aerobic exercise. Key points are that time constants of drainage of circulatory compartments put limits on peak flow in a pulsatile system. Muscle contractions increase the rate of return to the heart and by doing so prevent accumulation of volume in the muscle compartment and greatly increase circulatory capacity.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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