ARTERIAL DIAMETER AND MEAN BLOOD VELOCITY MEASUREMENTS
Bibliographic record
Abstract
The purpose of this study was to determine: 1) inter-observer difference in arterial diameter (AD) measures; 2) femoral AD changes during exercise; and 3) variability of mean blood velocity (MBV) measurements. Seven subjects (23 ± 2 yr) performed 4–6 repeats on different days of constant-load single-leg knee extension exercise transitions from unloaded exercise to a work rate eliciting 80% of VO2 peak, followed by loadless recovery. Doppler ultrasound was used to image the femoral artery during one trial for each subject, and to measure MBV on each trial. AD measurements were made independently by two investigators at rest and every 2 min throughout the test. MBV data were averaged over 2 s intervals (1 contraction cycle) and 10 s averages were taken during the first and last min of each phase of the trial to test for day-to-day variability. Mean AD measurements across all subjects and conditions were highly correlated (r = 0.93, r2 = 0.86) and were not significantly different between the two observers (8.2 ± 1.0 mm vs 8.4 ± 1.2 mm, mean ± SD). AD did not change significantly from rest values at any time point. The coefficient of variation (CV) for MBV day-to-day variability at any one time point ranged from 7.1 to 38.5% and the mean CV across all subjects and time points was 20.7%. Variability was largest during unloaded exercise, but similar during heavy exercise and recovery. These data demonstrate that femoral artery diameter does not change significantly from rest through to heavy exercise and any rest-exercise difference is similar in magnitude to that of inter-observer differences. Furthermore, because of the variability in femoral artery MBV, we recommend the use of repeated trials to obtain an accurate MBV. Supported in part by NSERC, Canada
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".