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Record W2327271688 · doi:10.1249/mss.0b013e3182632585

Aortic Stiffness Increased in Spinal Cord Injury When Matched for Physical Activity

2012· article· en· W2327271688 on OpenAlexafffund
Aaron A. Phillips, Anita T. Coté, Shannon S. D. Bredin, Andrei V. Krassioukov, Darren E. R. Warburton

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineSpinal cord injurySpinal cordPhysical medicine and rehabilitationCardiology

Abstract

fetched live from OpenAlex

PURPOSE: The objective of this study is to compare arterial stiffness between those with spinal cord injury (SCI) and able-bodied (AB) individuals when matched for habitual level of physical activity. METHODS: A total of 17 SCI and 17 AB individuals were matched for sex, age, weight, blood pressure, and levels of self-reported habitual physical activity (Godin-Shephard). Measures included central pulse wave velocity (PWV) (carotid-femoral PWV (cfPWV)) and lower limb PWV (femoral--toe PWV(ftPWV)) as well as large and small arterial compliance. RESULTS: The cfPWV was significantly elevated (7.3 ± 2.1 vs. 5.7 ± 1.4 m·s, P < 0.05) in SCI compared with AB. No other measures of arterial stiffness were different between the groups. Moderate to vigorous physical activity was significantly correlated with both large (r = 0.48, P < 0.05) and small (r = 0.65, P < 0.01) artery compliance, but not cfPWV or ftPWV. CONCLUSIONS: Both large and small artery compliance appear to be associated with habitual physical activity in physically active individuals with SCI. However, we did not show that physical activity is associated with PWV in physically active individuals with SCI. These findings suggest that factors other than physical inactivity may mediate the increase in arterial stiffness widely reported in the SCI population.

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)0.003
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 score0.558
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.354
Teacher spread0.328 · 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

Citations45
Published2012
Admission routes2
Has abstractyes

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