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The Influence of Training Status on Vascular Stiffness Following an Ultra-Endurance Event

2015· article· en· W2460673402 on OpenAlexaff
Holly Wollmann, Alis Bonsignore, Shannon S. D. Bredin, Barb Morrison, Lauren Buschmann, Josh Robertson, Darren E. R. Warburton

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineArterial stiffnessCardiologyApplanation tonometryArteryCompliance (psychology)Internal medicineEndurance trainingPhysical therapyBlood pressurePsychology

Abstract

fetched live from OpenAlex

PURPOSE: Recent evidence suggests that vascular stiffness increases following ultra-endurance events (>42. 2 km). However, it is unknown if training status affects vascular stiffness before and after ultra-endurance events. The purpose of this research was to examine whether training status influences vascular stiffness before and after an ultra-endurance event. METHODS: Thirty male recreational ultra-endurance runners (41.6±10.8yrs) were separated evenly into three groups according to self -reported training volume (km·week-1). The groups were defined as: Group 1: 25-55, Group 2: 60-75 and Group 3: 80-110 (km·week-1). Arterial compliance (radial applanation tonometry (CR-2000, HDI)) was measured at rest and within 30 minutes following their individual race event lengths of 50 miles, 70 miles or the 120 mile ultra-endurance trail run. RESULTS: There was no significant difference in resting large artery (18.8 ± 9.5 mL· mmHg-1 x 10) or small artery (7.5 ± 3.0 mL· mmHg-1 x 100) (p > 0.05) compliance between the three training groups at baseline Large artery compliance baseline measurements per group were recorded as group 1(25.3±12.9), group 2(14.6 ±3.6) and group 3 (16.2±4.4) (mL· mmHg-1 x10). Small artery compliance baseline measurements per group were recorded as group 1 (8.6±2.6), group 2 (7.6±3.6) and group 3(6.3±2.4) (mL· mmHg-1 x100). There was also no significant difference between groups in the relative changes in large artery (-1.2±9.5 mL· mmHg-1 x10) or small artery compliance (-0.03±3.5 mL· mmHg-1 x100) (p > 0.05) seen after the ultra-endurance event. Average change in large arterial compliance showed minimal differences between groups; group 1 (2.3±4.5), group 2 (-2.6±11.9) and group 3(-3.4±10.9) (mL· mmHg-1 x 10). Average change in small arterial compliance showed minimal differences between groups, group 1(-1.9±3.8), group 2 (0.94±4.1) and group 3 (0.86±2.11) (mL· mmHg-1 x100). CONCLUSION: The weekly volume of training engaged upon in ultra-endurance athletes does not appear to differentially affect the baseline arterial stiffness or changes in arterial stiffness following an ultra-endurance event.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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