Peak vs. total reactive hyperemia: which determines the magnitude of flow-mediated dilation?
Bibliographic record
Abstract
We investigated the independent contributions of the peak and continued reactive hyperemia on flow-mediated dilation (FMD). 1) For the duration manipulation experiment (DME), 10 healthy males experienced reactive hyperemia durations of 10 s, 20 s, 30 s, 40 s, 50 s, or full reactive hyperemia (RH). 2) For the peak manipulation experiment (PME), eight healthy males experienced reactive hyperemia trials with three peak shear rate magnitudes (large, medium, and small). Data are means +/- SD. For the DME, peak shear rate was not different between trials (P = 0.326). Shear rate area under the curve (AUC) was P < 0.001. Peak %FMD was dependent on shear rate AUC: 10 s, 2.7 +/- 1.3; 20 s, 6.2 +/- 1.9; 30 s, 7.9 +/- 2.9; 40 s, 8.3 +/- 3.2; 50 s, 7.9 +/- 3.2; full RH, 9.3 +/- 4.1, with 10 and 20 s less than full RH (P < 0.001). For the PME, peak shear rate was different between trials (large, 1,049.1 +/- 285.8; medium, 726.4 +/- 228.8; small, 512.8 +/- 161.8; P < 0.001). AUC of the continued shear rate was not (P = 0.412). Peak %FMD was unaffected by peak shear rate (large, 7.0 +/- 2.7%; medium, 7.4 +/- 2.6%; small, 6.6 +/- 1.8%; P = 0.542). Peak and AUC shear stimulus were not significantly related in full RH (r(2) = 0.35, P = 0.07). We conclude that the shear stimulus AUC, not the peak itself, is the critical determinant of the peak FMD response. This indicates AUC as the best method of quantifying reactive hyperemia shear stimulus for %FMD normalization.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".