Reliability of Vasodilation in Response to Passive Leg Movement in Young, Healthy Women
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".