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Reliability of Vasodilation in Response to Passive Leg Movement in Young, Healthy Women

2021· article· en· W3170853273 on OpenAlexafffund
Lindsay A. Lew, Kaitlyn Liu, Kyra E. Pyke

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntraclass correlationMedicineVasodilationReliability (semiconductor)Coefficient of variationInternal medicinePhysical therapyMathematicsStatistics

Abstract

fetched live from OpenAlex

Passive leg movement (PLM) elicits rapid vasodilation in the microvasculature that is primarily dependent on nitric oxide. PLM‐induced vasodilation (PLM‐D) therefore, provides an index of lower‐limb microvascular endothelial function. PLM‐D is lower in advanced aging and clinical populations vs. young and healthy controls, demonstrating that PLM‐D provides a useful assessment of vascular responses. However, there is currently limited information regarding the reliability of PLM‐D and no information on the reliability of PLM‐D in women. Therefore, the purpose of this study was to test the hypothesis that PLM‐D in women is reliable when measured over two separate days. Seventeen, young healthy women (22 ± 3 yrs) participated in two identical experimental visits during the early follicular (low‐estrogen) phase of the menstrual cycle. Each visit included three standardized trials of PLM involving one minute of baseline, two minutes of PLM and two minutes of recovery. During the two minutes of PLM an experimenter moved the participant's leg from 90° flexion to 180° extension and back at a set pace of 1 cycle/s. Using duplex ultrasound PLM‐D was characterized by six commonly utilized factors; peak leg blood flow (LBF) and vascular conductance (LVC), peak change above baseline (Δpeak) for LBF and LVC, and area under the curve above baseline (ΔAUC) during the first 60s of PLM for LBF and LVC. The day‐to‐day reliability of PLM‐D was quantified by calculating the Pearson correlation coefficient (r value), intraclass correlation coefficient (ICC) and coefficient of variation (CV). The results demonstrated good day‐to‐day reliability of PLM‐D characterized as peak LBF (r = 0.84, p < 0.001; ICC = 0.84; CV = 13.2%), peak LVC (r = 0.82, p < 0.001; ICC = 0.79; CV = 14.4%), Δpeak LBF (r = 0.83, p < 0.001; ICC = 0.82; CV = 17.8%) and Δpeak LVC (r = 0.83, p < 0.001; ICC = 0.80; CV = 16.5%). Characterization of PLM as ΔAUC demonstrated moderate day‐to‐day reliability; ΔAUC LBF (r = 0.71, p< 0.05; ICC = 0.70; CV = 31.2%) and ΔAUC LVC (r = 0.78, p < 0.001; ICC = 0.74; CV = 27.1%). In conclusion, this study demonstrates that PLM‐D has good day‐to‐day reliability, however, characterization of PLM‐D as peak and Δpeak LBF and LVC is more reliable than ΔAUC. These findings support the use of PLM‐D in future studies as a reliable method to assess lower‐limb microvascular endothelial function in women.

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)0.001
metaresearch head score (Gemma)0.005
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
Metaresearch0.0010.005
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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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
Published2021
Admission routes2
Has abstractyes

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