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Cardiac responses to progressive exercise in normal children: a synthesis

2000· article· en· W1973169907 on OpenAlexaff
Thomas Rowland, James E. Potts, Terri Potts, G Sándor, DONNA GOFF, Lisa Ferrone

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsCardiologyStroke volumeMedicineInternal medicineContractilityCardiac outputHeart rateAfterloadCardiac cycleStroke (engine)DiastoleHemodynamicsBlood pressure

Abstract

fetched live from OpenAlex

UNLABELLED: The cardiac responses to exercise are influenced by a complex interplay of changes in diastolic filling, intrinsic myocardial contractility, heart rate, and ventricular afterload. PURPOSE: To characterize these responses in children, findings are reported from two studies utilizing Doppler echocardiographic assessment of stroke volume and cardiac output during maximal upright and semisupine cycle exercise. METHODS: In study 1, stroke volume, heart rate, and peak aortic velocity were assessed during upright cycle exercise to exhaustion in 39 sixth-grade boys. In study 2, similar methods were used to examine cardiac responses to semisupine exercise with measurement of left ventricular dimensions by two-dimensional echocardiography. RESULTS: The findings support patterns similar to that previously described in adults, with an initial rise in stroke volume reaching a plateau at mild-moderate exercise intensities. CONCLUSIONS: The observations in these two studies also suggest 1) peripheral vasodilatation plays an important role in the early rise in stroke volume, 2) increasing heart rate acts to maintain a stable stroke volume and left ventricular diastolic dimension at high workloads, and 3) improvements in contractility serve to maintain stroke volume as the systolic ejection period shortens.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 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

Citations33
Published2000
Admission routes1
Has abstractyes

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