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Record W2320848726 · doi:10.1055/s-0033-1358478

Temporal Response of Arterial Stiffness to Ultra-Marathon

2014· article· en· W2320848726 on OpenAlexafffund
Jamie F. Burr, Arlie Phillips, Taylor Drury, Adam Ivey, Darren E. R. Warburton

Bibliographic record

VenueInternational Journal of Sports Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of British Columbia HospitalUniversity of British ColumbiaUniversity of Prince Edward Island
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsArterial stiffnessPulse wave velocityMedicineCardiologyApplanation tonometryInternal medicineAthletesArteryFemoral arteryRadial arteryCompliance (psychology)Physical therapyBlood pressurePsychology

Abstract

fetched live from OpenAlex

The purpose of this investigation was to characterize the arterial stiffness of male ultra-marathon runners (n = 9) using pulse wave velocity (cfPWV) and radial tonometry over the course of an ultra-marathon and during recovery. Measures were collected at rest, immediately following 45 km/75 km of running, then following 60 and 90 min of recovery. No statistical difference was found between baseline cfPWV and normative values. The cfPWV of ultra-endurance runners decreased at 45 km (3.4 ± 1.6 m/s, p=0.006), followed by an increase (1.6 ± 1.8 m/s, p = 0.04) toward baseline levels at the 75 km mark. Radial tonometry measures also indicated small artery stiffness was transiently increased after 75 km. The amount of training time (r = 0.82, p = 0.007) and the duration of a typical training session (r = 0.73, p = 0.03) were correlated strongly with persisting decrements in large artery compliance at 60 min of recovery. The finding that arterial stiffness decreased at the 45 km distance and then reverted back toward baseline levels with prolonged running, may indicate a role of exercise duration or accumulated stress for affecting vascular compliance. At present, it is premature to suggest that athletes should alter training or racing practices to protect vascular health.

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)0.002
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 score0.914
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.012
GPT teacher head0.316
Teacher spread0.304 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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