Trial‐to‐Trial and Day‐to‐Day Variability in Forearm Blood Flow During Reactive Hyperemia
Bibliographic record
Abstract
Reactive hyperemia (RH) following the release of a brief limb occlusion is commonly used to assess resistance vessel function. The purpose of this study was to examine the impact of repeated trials on the within subject day‐to‐day variability of forearm blood flow during RH. Ten young, healthy, non‐smoking subjects (6 female, 4 male) were examined in the fasted state. Brachial artery diameter and blood velocity were assessed with Echo and Doppler ultrasound, respectively. Subjects visited the lab twice and performed four RH trials each visit. The RH protocol was performed on the left arm and included 5 minutes of forearm cuff occlusion at a pressure of 250 mmHg, followed by release. Within subjects trial‐to‐trial and day‐to‐day variability was assessed by the coefficient of variation (CV = (Standard Deviation/Mean) x 100). The Day‐to‐day CV was calculated using 1) Trial 1 from each day and 2) a 4‐Trial average. Peak forearm blood flow did not differ significantly between trials or days (p > 0.05). Within subject trial‐to‐trial variability was consistent between visits (CV, 9.39 ± 7.78% and 10.1 ± 3.19%; p = 0.737). The within subject day‐to‐day CV determined from the first trial on each day was not significantly different from the CV determined from the four trial average (CV, 14.9± 15.1% and 12.0± 9.62%; p = 0.240). In conclusion, averaging over repeated trials on each visit did not significantly reduce the day‐to‐day variability in peak reactive hyperemia blood flow.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".