Impact of repeated increases in shear stress via reactive hyperemia and handgrip exercise: no evidence of systematic changes in brachial artery FMD
Bibliographic record
Abstract
Reactive hyperemia (RH) creates an uncontrolled, transient increase in brachial artery (BA) shear stress (SS) for flow-mediated dilation (FMD) assessment. In contrast, handgrip exercise (HGEX) can create similar, sustained SS increases over repeated trials. The purpose of this study was to examine the impact of repeated SS elevation via RH or HGEX and the relationship between RH and HGEX %FMD. BA diameter and blood velocity were assessed with echo and Doppler ultrasound in 20 healthy subjects. Visit A consisted of four 6-min HGEX trials (HGEX trials 1-4) at the intensity required to achieve a shear rate (SR = mean blood velocity/BA diameter; an estimate of SS) of 65 s(-1). Visit B consisted of four RH trials (RH trials 1-4). The RH SR area under the curve (AUC) was higher in trial 1 versus trial 3 and trial 4 (P = 0.019 and 0.047). The HGEX mean SR was similar across trials (mean SR = 66.1 ± 5.8 s(-1), P = 0.152). There were no differences in %FMD across trials or tests (RH trial 1: 6.9 ± 3.5%, trial 2: 6.9 ± 2.3%, trial 3: 7.1 ± 3.5%, and trial 4: 7.0 ± 2.8%; HGEX trial 1: 7.3 ± 3.6%, trial 2: 7.0 ± 3.6%, trial 3: 6.5 ± 3.5%, and trial 4: 6.8 ± 2.9%, P = 0.913). No relationship between subject's RH %FMD and HGEX %FMD was detected (r(2) = 0.12, P = 0.137). However, with response normalization, a relationship emerged (RH %FMD/SR AUC vs. HGEX %FMD/mean SR, r(2) = 0.44, P = 0.002). In conclusion, with repeat trials, there were no systematic changes in RH or HGEX %FMD. The relationship between normalized RH and HGEX %FMD suggests that endothelial responses to different SS profiles provide related information regarding endothelial function.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".