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Record W2785183088 · doi:10.1055/s-0043-121272

Muscle Oxygenation Responses to Low-intensity Steady Rate Concentric and Eccentric Cycling

2018· article· en· W2785183088 on OpenAlexaff
Mark Rakobowchuk, Laurie Isacco, Ophélie Ritter, Alicia Represas, Malika Bouhaddi, Bruno Degano, Nicolás Tordi, Laurent Mourot

Bibliographic record

VenueInternational Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsThompson Rivers University
FundersDivision of Electrical, Communications and Cyber SystemsAgence Nationale de la Recherche
KeywordsEccentricHeart rateMedicineCardiologyConcentricOxygenationCyclingInternal medicineCardiac outputStroke volumeBlood pressureAnesthesiaMathematics

Abstract

fetched live from OpenAlex

Muscle deoxygenation responses provide information about the training impulse of an exercise session enabling adaptation to be predicted. Our aim was to investigate muscle oxygenation profiles during prolonged low-intensity eccentric and concentric cycling. Twelve healthy men performed two 45-min exercise sessions of concentric (CON) and eccentric (ECC) cycling, matched for the same heart rate at the start of each session. Mechanical power output during ECC was ~2.5 times that of CON (210±40 W vs. 82±16 W). Oxygen uptake, blood lactate, cardiac output and systolic arterial pressure responses did not differ between exercises. Heart rate was similar at 5 min of each exercise bout but progressively increased during ECC and was higher at 15, 30 and 45 min of ECC compared to CON (+10 bpm), with a trend for a lower stroke volume. Diastolic and mean blood pressures were higher during ECC. No significant differences were observed in muscle oxygenation profiles. Muscle oxygenation responses during prolonged low-intensity exercise were not affected by the type of muscle action at the same metabolic demand and cardiac output.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.889
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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 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

Citations16
Published2018
Admission routes1
Has abstractyes

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