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Record W2560181322 · doi:10.1080/17461391.2016.1262453

The influence of race length on arterial compliance following an ultra‐endurance marathon

2016· article· en· W2560181322 on OpenAlexaff
Alis Bonsignore, Shannon S. D. Bredin, Holly Wollmann, Barb Morrison, Andrew T. Jeklin, Lauren Buschmann, Josh Robertson, E. Jean Buckler, Duncan McGuinty, Mark S. Rice, Darren E. R. Warburton

Bibliographic record

VenueEuropean Journal of Sport Science · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArterial stiffnessMedicineCompliance (psychology)CardiologyApplanation tonometryInternal medicinePhysical therapyBlood pressurePsychology

Abstract

fetched live from OpenAlex

There is inconclusive evidence concerning the effects of routine participation in ultra-endurance events on cardiovascular disease (CVD) risk. Arterial compliance is a reliable, non-invasive, and effective tool for evaluating CVD risk. The purpose of this research was to examine if race length influences acute changes in arterial compliance following an ultra-marathon event. A total of 46 ultra-marathon runners were recruited including 21 participants (39.8 ± 8.3 years, 6 females) in the 80-km event and 25 participants (43.7 ± 9.8 years, 3 female) in the 195-km event. Arterial compliance was measured via radial applanation tonometry (CR-2000, HDI) for diastolic pulse contour analysis before and following the race. Significant between-group differences were found for changes in large arterial compliance with a decrease (increase in stiffness) following the 195-km event and an increase following the 80-kilometre event (p < .05). Longer race lengths are associated with greater reductions in large arterial compliance following recreational ultra-marathon running. Assessment of arterial compliance might be a useful prognostic tool to assess the long-term risk of CVD among ultra-marathon runners.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.023
GPT teacher head0.298
Teacher spread0.275 · 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
Published2016
Admission routes1
Has abstractyes

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