Characterization and reproducibility of forearm arterial flow during reactive hyperemia
Bibliographic record
Abstract
Peripheral arterial flow has been assessed for a variety of indications including characterization of endothelial function during reactive hyperemia. However, quantification of this blood flow as a surrogate remains an imperfect reflection of endothelial function. We sought to better characterize hyperemic reaction to (1) elucidate the influence of the endothelial function and (2) assess the reproducibility of our modeling over time. Sixteen normal subjects underwent simultaneous forearm reactive hyperemia testing with a near-infrared system at baseline, baseline +24 h and baseline +27 h. Baseline flow was measured to 3.6 +/- 0.2 ml dl(-1) min(-1), and was highly reproducible 24 and 27 h later. With reactive hyperemia, the blood flow increased to 20.5 +/- 4.6 ml dl(-1) min(-1). Arterial blood flow curves during reactive hyperemia displayed a bimodal pattern, with the second peak occurring 59.1 +/- 10.6 s after the onset of hyperemia. We believe that this latest peak represents the contribution of endothelial factors to the hyperemic reaction. Modeling of hyperemic curves led to the introduction of a reproducible new parameter (etafactor) that reflects the normalized contribution of this second peak. In conclusion, forearm arterial flow during reactive hyperemia revealed a bimodal distribution where functional interpretation allowed distinction of the two components. Basal flow measurements and results of this modeling were reproducible 24 and 27 h later.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".