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Record W2026143307 · doi:10.1055/s-0031-1297956

Systemic Arterial Compliance Following Ultra-Marathon

2012· article· en· W2026143307 on OpenAlexaff
Jamie F. Burr, Shannon S. D. Bredin, Aaron A. Phillips, Heather J.A. Foulds, Anita T. Coté, Sarah Charlesworth, Adam Ivey, Taylor Drury, Renée J. Fougere, Darren E. R. Warburton

Bibliographic record

VenueInternational Journal of Sports Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCompliance (psychology)MedicineCardiologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

There is a growing interest in training for and competing in race distances that exceed the marathon; however, little is known regarding the vascular effects of participation in such prolonged events, which last multiple consecutive hours. There exists some evidence that cardiovascular function may be impaired following extreme prolonged exercise, but at present, only cardiac function has been specifically examined following exposure to this nature of exercise. The primary purpose of this study was to characterize the acute effects of participation in an ultra-marathon on resting systemic arterial compliance. Arterial compliance and various resting cardiovascular indices were collected at rest from 26 healthy ultra-marathon competitors using applanation tonometry (HDI CR-2000) before and after participation in a mountain trail running foot race ranging from 120-195 km which required between 20-40 continuous hours (31.2±6.8 h) to complete. There was no significant change in small artery compliance from baseline to post race follow-up (8.5±3.4-7.7±8.2 mL/mmHgx100, p=0.65), but large artery compliance decreased from 16.1±4.4 to 13.5±3.8 mL/mmHgx10 (p=0.003). Participation in extreme endurance exercise of prolonged duration was associated with acute reductions in large artery compliance, but the time course of this effect remains to be elucidated.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.314
Teacher spread0.290 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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